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From the press blurb:. The volume examines animation from a spatial lens. It offers interdisciplinary outlooks to the role of space in animation, including in creating humorous moments in early cartoon shorts, generating action and suspense in Japanese anime, and even stimulating erotic pleasure in pornographic Hentai. Animation, in this book, is approached as a medium that can equip the designers of the built environment with a utopian scope to address our socio-political and ecological crises. Comparison of the visual grammar of the multiplane camera and the Disneyland dark ride. And as with my prior publications, I enjoy executing my own illustrations. More information about the other contributors and their essays can be found at Liverpool University Press. Gregory Turner-Rahman. As seen prior, during the summer of and I was able to visit the Disneyland Resort and continue my site documentation photography. The lettering and application certainly does not date from If I recall correctly, I first remember seeing this mark in the s, and it has been used on merchandise ever since. The signage which dates to the opening of the land in is really something special, like this above example from table service restaurant Cafe Orleans. From the late s into the mids, design was going through many period revivals , one of which was Art Nouveau. Since France was a center of the turn-of-the-century Art Nouveau movement, the Imagineers must have thought, okay, French, New Orleans, makes sense. Hand-painted floral illustrations and stylized serif lettering demonstrate this sensibility throughout, like on the wall outside the Mint Julep Bar. The late s was the height of the phototypesetting era , and this poster-size sign which has sat at the entrance to Pirates of the Caribbean since it opened on March 18, , provides a solid bouquet of the kinds of offerings that were popular in phototype catalogs. Along with Art Nouveau, typefaces from the Victorian era were being revived at this time. You might recognize it as the title face for the television series Murder She Wrote or from the album cover for Siamese Dream by The Smashing Pumpkins. The serif treatment of the attraction name here matches other nearby applications, but I suspect the sign dates from the s or even the s with the line treatment of the skull and crossbones. Some signage is more playful than historical, as at that same gift shop just off the exit to Pirates , Pieces of Eight. Much of the safety information in the park dates to the s, but the designers have usually done a good job of making substrate and printing choices that align with the overall theme. This is a contemporary, digital italic serif, yet it fits with period samples like The Fell Types which are used to evoke the swashbuckling era. This sign probably dates to the late s or early s. Hand painted Roman type abounds in NOS. Yet the silhouette of the man with the top hat? And here is such a sign. Too extreme. Like so many signs at Disneyland, it looks wonderful at night and takes on a different personality after dark. Since , The Blue Bayou has featured a very elegant and stately double-B monogram of the Shelley variety on their menus, but this particular application is relatively recent, within the last ten or fifteen years. It looks more at home on a Prince album cover from the s than at the classiest table service general admission eatery in the park. Behold the original entrance plaque to the mysterious Club I have always absolutely adored this double-3 mark. The numeral forms are quite unique, they harmonize with the double-B monogram directly next door at The Blue Bayou, and they inject a bit of midth century modernism into an otherwise 19th century setting. Very s executive class, very Mad Men. Still, the remodeled club, which I visited in July of , is exquisite and tasteful. The revised mark looks absolutely stunning in mosaic tile at its entrance. This plaque sits at its entrance, and has roots in a Victorian woodcut typeface called Rubens which was a popular phototype revival during the s. Designed by John F. Cumming in the s, every major foundry of the era featured a cut of it in several weights. You might recall it being used in the opening titles for the s series Knight Rider. More recent entry and safety signage from the late s and early s employs Runic , a Monotype face that dates back to The Imagineers made a good call here; to my eye Runic vibes well with Rubens. Victorian serifs of a similar character as Rubens can be found scattered throughout NOS. This painted lettering appears to be based on a phototype revival of a face from the s called Jefferson. Similar serifs from the same era include Washington , Webster , and Lafayette. As far as I can tell, this shop dates back to This makes me think that the graphic designers at Imagineering might have been working on typography for the land for quite a while, and made many of the same phototype selections within the same time frame. Wonderful custom swashes; this might be my favorite sign in the entire land. The lettering is also repeated in a painted graphic on a nearby wall. Some of the newer pieces in NOS are as detailed and lovely as the original material. This entrance sign dates to the late s or early s, and just strikes me as cheap looking. The Roman lettering is straight out of Adobe Illustrator, with amateurish dotted strokes set outside the rounded characters. And the ornamented cross at the bottom is almost falling off the sign. Some of tackiest stuff in NOS dates to the early s and is tied to the branding for the Pirates of the Caribbean film series, which features some terrible typography in its posters and marketing materials. But Imagineering appears to have recovered from that sad era. In fact, nearly every retail and dining space in NOS has a numbered street address. You can find them painted in a variety of lettering styles throughout. It certainly feels I was fortunate enough to dine at 21 Royal in June of Though I took plenty of pictures, the space was devoid of typography for the most part. Originally, this space above Pirates was intended to be a private apartment for Walt and Roy Disney, then it was used as the Disney Gallery before being converted into a VIP overnight experience and finally a exclusive dining one. You see one version or another of this D all over the park. They vary somewhat based on the year. This one is on a popcorn vending cart just outside The Haunted Mansion. Speaking of popcorn carts, there is some nifty recent lettering on the NOS ones. That appears to be what we have here. This looks very close to Prince. Of course any designer worth their paycheck is going to try and customize a typeface when they can, and this may have happened here. Here on the side of the cart we have more Letterhead loveliness. New Orleans Square has always shared a Disneyland Railroad station with the adjacent Frontierland since its opening, so in deference to that elder, opening day land, the lettering on its queue structure is appropriately Old West. One of the pleasures of walking around Disneyland as a graphic designer is taking in all the signage, much of which is—still, to this day—hand painted or hand applied. These typographic delights, usually quite well-attuned to a given time and place by the Imagineers, are but one small part of the gestalt of visual details which make themed spaces so immersive and engaging. Typography—that is to say, the use of lettering on signage and props—is an essential part of world building within themed spaces. This constructed script was first used on screen displays in Return of the Jedi as designed by Joe Johnston , an art director on the film. He called the original character set he developed Star Wars 76 , and it has been modified several times since for use within the Star Wars universe of films, television series, and merchandise. Aurebesh is not the only alien language in the Star Wars universe, however. This entrance sign is masterfully rendered in one of these other alphabets. Equally impressive is the ways in which English is incorporated throughout the land. Sometimes the use of English is charmingly and deceptively non-obvious, as at the entrance to Oga's Cantina. Many guests might walk right past and not read it because at a glance the lettering could be an alien language. Look closely, however, and the Roman characters emerge. What does this say? Simply point your smartphone at the signage. The variety of applications throughout the land is very cool, and all are appropriately distressed. The Disney Imagineers have different level of graphics which they apply in their placemaking. The first, and more important, are Operational Graphics. These need to be in the native language of the guests, and, though stylized, fairly legible. A large subset of these are wayfinding , just like the directional signage you would find at a mall or a large airport. Here the subtitles for each location are not required, and the titles can be clearly read in English. The stylization of operational graphics is a delicate business. Here the wait time indicator at Millennium Falcon: Smugglers Run needs to be loud and clear for guests. Notice that the lettering is subtly bolder with less flourishes than the earlier example. Like any other visual hierarchy, scale undoubtedly helps. This exit sign for the attraction is quite stylized, but painted very large on the wall. Just like in the real world, repetition also helps with recognition. A common challenge with operational graphics is when they intersect with regulatory requirements. This restroom meets the international standard for the MEN icon while still feeling part of the world of Star Wars. Similarly, this working fire hydrant needs the appropriate labeling but is stylized to the rest of the operational English as seen throughout the land. Perfect theming is not always possible, of course. The second category are Story Graphics. Like the name of the entrance to a shop or eatery, these bits of signage reinforce the reality of the narrative being established by the space. Here is a parking sign like you would find in an actual urban environment. Successful story graphics express vernacular, or the recognizable and credible look of existing solutions and systems. This flight space announcement appears just as it would at a bus depot, subway station, or airport terminal. Is there Coke in Star Wars? The final category are Ghost Graphics. Ghost graphics are also applied to objects. This is the definition of ghost —these graphics float around almost invisible in the background, but add reality nonetheless. Sometimes ghost graphics can subtly support story objectives. This pair of storage tanks is a ideal example. Imagineers often use ghost graphics to establish the history of a space; to give it the sense that there have been many different layers of inhabitation over the years. Here some kind of signage has faded away in the hot sun, and new lettering has been written on top in a kind of graffiti. And of course all three types of graphics may contain iconography. On this story graphic, however, the meaning of the symbol is unknown to us; it serves no function. For example, within the Millennium Falcon set there is an XO on the wall which only serves as a reference to where Han Solo and Princess Leia shared a brief kiss. Whether operational, story, or ghost, all the graphic design and typography within a themed environment contributes to the immersion and inhabitation we experience. Disney has a short video from their Imagineering in a Box series that covers many other examples from their parks if you are interested in learning more. And with good reason! Here you will find multiple, sometimes unexpected entry points to virtuality—theme parks, video games, gyms, pilgrimage sites, art installations, screens, drones, film, and even national identity. Book One: When Worlds Collide. Book Three: Senses of Place and Space. The project began in the summer of and at long last went was released on December 15, As our press blurb states:. Contemporary virtual reality is often discussed in terms of popular consumer hardware. Yet the virtual we increasingly experience comes in many forms and is often more complex than wearable signifiers. This three-volume collection of essays examines the virtual beyond the headset. Virtual Interiorities offers multiple, sometimes unexpected entry points to virtuality—theme parks, video games, gyms, pilgrimage sites, theater, art installations, screens, drones, film, and even national identity. What all these virtual interiorities share are compelling cultural perspectives on distinct moments of environmental collision and collusion, liminality, and shifting modes of inhabitation, which challenge more conventional architectural conceptions of space. My own chapter in Virtual Interiorities appears in Book Three and is an expanded version of an article I published this past year in the latest issue of Disegno—The Journal of Design Culture. In addition to writing and co-editing, I designed the book covers with Greg. He provided the imagery and I supplied the typography and layout. One particular challenge which was very rewarding was having control over the internal typography and layout of the book. ETC uses the Pressbooks platform for electronic and print-on-demand formatting. By extensively modifying that code, I was able to customize every class of type in the book, and even control positioning on the page throughout the layout. This was extremely useful for front and tail matter which required extensive visual hierarchy. I had never used CSS to such detailed extent before, and certainly never in page layout for print. It was a lot of fun. Since this is very much an image blog, also here are the illustrations I created to accompany the article and subsequent chapter. Bazin used the Olympic Theater of Vincenza as his example of how the architecture of the stage functions as an internal world to keep it isolated from reality outside. The LED surfaces not only display content, they also provide realistic lighting with adjustable color. What Bazin could not foresee was how media would shift from passive to active, and how theater and cinema would become a new, single medium of participatory interaction. So pleased and proud that Virtual Interiorities is now a reality! The original Tower of Terror standing tall over a bug's land , Fan reaction to Avengers Campus was mixed to negative. Avengers Campus , satellite view. Click for link. The land consists of two major attractions, a flagship restaurant, and frankly not much else. Apparently the Imagineering vision was initially more expansive but the project was subject to budget cuts in the face of the global coronavirus pandemic. I have not seen any of the Marvel Cinematic Universe films all the way through. I started watching the first Iron Man with my nephew once, but I lost interest. Yet successful thematic design means providing immersion and interest without presuming guests are familiar with the underlying intellectual property. This map of the Avengers Campus is thus appropriately Art Deco, and it introduces the backstory that the area grew out of a complex of Stark defense plants and research labs. The first thing I noticed as I walked through Avengers Campus were scores of ghost graphics. Their purpose is to provide a faux historicity to a themed space. Just like with the actual built environment, layers accumulate over time. Ghost graphics, particularly larger ones, can create the impression that a space is older than it is. This sign is a reference to Edwin Jarvis, loyal household butler to the Stark family. Web Slingers show building, satellite view. The footprint of its show building takes up more space than any other structure in the land. The mark appears to be an abstraction of a spider and is featured on signage throughout. Part of the backstory of the attraction is that Tony Stark is holding an open house event at WEB, so many of the supporting graphics have a very trade show look and feel. There are small details all around the show building structure which contribute to the overall immersion. All the typographic is organized and systematic. As within all Disney theme park lands, even the trash and recycle cans are appropriately branded. All sheen red metal. I wonder if there is a subtle commentary here, that behind the shiny corporate image of innovation and technological wonder is the the costs—environmental devastation and pollution in the form of pipes, smokestacks, and rusted shed metal. They are helpers which run amok and begin self-replicating out of control. This one appears to have been shot with one of Spider-Mans webs and is disabled. I found this split between slick corporate trade show and industrial machinery to be the most interesting design element of the land. One of the mistakes the Imagineers made, however, was to try and cram a bunch of different aspects of the Marvel Cinematic Universe into this small themed area. A version of the Disney Magic cruise ship stage show Doctor Strange: Journey into the Mystic Arts is performed here by a costumed cast member portraying the titular character from Doctor Strange For as long as anyone can remember, rumors of unexplained events and energies have emanated from a remote location in the hills outside Los Angeles. In the late s, a Stark Industries complex was built on the location. And its inclusion within the campus feels pretty forced. The theming is pretty cool, but the whole thing comes off as both a missed opportunity and a franchise checkbox. Given a more robust backstory or even an attraction, maybe. This is just a courtyard for character photo ops. And one novelty photo. Off to one side is an optical illusion rendered on a tiled floor. Custom made for Instagram for sure. It just feels cheap, hokey. I do want to commend the designers here for their sense of transition. The wall with fencing is a perfect break from the theme guests are arriving from, forming a natural threshold and gateway to Avengers Campus. The sitting vehicle as oversized prop, however, scans as lazy design. This is a Universal Studios move and should be beneath the Imagineers. Turning to the right from the Cars Land entrance, we find the Avengers Headquarters. None of the above. Back of house for future expansion, satellite view. Originally this was to be another banner attraction, an elaborate 4D dark ride or roller coaster to be built on a parcel of land used for cast parking and back of house facilities. Walking through Avengers Campus from its primary entrance, the first attraction to be Marvelized at California Adventure is actually the final one you encounter. Guardians of the Galaxy — Mission: Breakout! Tower of Terror as seen from Condor Flats , Before the Carthay Circle Restaurant was built, you would see it from all sorts of vantages at the front side of the park. Some of the details like these new streetlights are well done. Once again, I had to look all this stuff up. Henry Jonathan 'Hank' Pym is a scientist who invents a substance which can change size, and becomes Ant-Man. There are some clever visual effects all around the space, like this factory assembly line of pretzels overhead. Typical store-bought pretzels enter some kind of Pym particle device with glowing energy rays, and emerge on the other size enlarged like the ones sold in the restaurant. Ordinary condiment bottles are oversized. Have we been shrunk? Or did Dr. Pym make them larger? Same with the self-serve soft drink counter. All the fluids appear to be supplied by massive cola cans mounted above. The graphics are all accurate and convincing. The outside bar, Pym Tasting Lab , continues the theme, with a single massive beer can behind the taps. One final touch. Here outside Pym Test Kitchen you can find what appear to be enlarged Christmas-style twinkle lights. I really wanted to like Avengers Campus. I like it when the designers succeed. Nice little bits here and there, but great thematic design produces a gestalt effect—it should feel greater than the sum of its parts. The bottom line is good storytelling is good storytelling and superior design is superior design. This concludes my nine part tour of all the changes made to Disney California Adventure, — Next up is everything new across the way at Disneyland Park and other parts of the resort. From the press blurb: The volume examines animation from a spatial lens. View fullsize. Purple Rain Bayou? Operational Graphics The Disney Imagineers have different level of graphics which they apply in their placemaking. As are the health warnings as required by California law. Story Graphics The second category are Story Graphics. Ghost Graphics The final category are Ghost Graphics. Book Three: Senses of Place and Space The project began in the summer of and at long last went was released on December 15, As our press blurb states: Contemporary virtual reality is often discussed in terms of popular consumer hardware. Table of Contents in CSS 4. Teatro Olimpico di Vicenza. Typical game engine design space. Stranger Things One of the mistakes the Imagineers made, however, was to try and cram a bunch of different aspects of the Marvel Cinematic Universe into this small themed area. According to the official Imagineering backstory, For as long as anyone can remember, rumors of unexplained events and energies have emanated from a remote location in the hills outside Los Angeles. Is this an attraction? A restaurant? A themed shop? The entry doors sit closed shut and unused. Guardians of the ToT Walking through Avengers Campus from its primary entrance, the first attraction to be Marvelized at California Adventure is actually the final one you encounter. Newer Older.
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HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. We introduce SODA, a self-supervised diffusion model, designed for representation learning. The model incorporates an image encoder, which distills a source view into a compact representation, that, in turn, guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder, and leveraging novel view synthesis as a self-supervised objective, we can turn diffusion models into strong representation learners, capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge, SODA is the first diffusion model to succeed at ImageNet linear-probe classification, and, at the same time, it accomplishes reconstruction, editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space, that serves as an effective interface to control and manipulate the produced images. All in all, we aim to shed light on the exciting and promising potential of diffusion models, not only for image generation, but also for learning rich and robust representations. See our website at soda-diffusion. What I cannot create, I do not understand. Synthesis, the ability to create, is considered among the highest manifestations of learning \[ 1 , 2 \]. As opposed to passive analysis of a text or an image, conceiving them out of thin air involves profound understanding of the underlying factors and intricate generative processes that give rise to the final product \[ 3 \]. Indeed, learning to write in a new language is often more challenging than reading it. Figuring out the solution to a math problem is fundamentally harder than verifying it \[ 4 \]. And just as the chef learns more about the culinary arts than the diner to prepare a tasty meal, and the novelist knows more about narrative structures than the reader to tell a good story, the artist better grasps perspective and composition to craft a breathtaking masterpiece. Analogously, in AI, the recent years have witnessed remarkable progress at the generative domain, with large-scale diffusion modeling proving to be a powerful and flexible technique that can create vivid imagery of astonishing realism and incredible detail. And yet, while the vast majority of research harnesses these models for the straightforward goal of synthesis or editing alone \[ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 \] , only little attention has been given to their representational capacity \[ 18 , 19 , 20 \] , leaving this promising direction rather unexplored. Surely, models that can weave from scratch such rich depictions of high fidelity, likely learn much along the way about the underlying properties, processes, and components that make up the resulting pictures. How then can we leverage this untapped potential of diffusion models for the purpose of representation learning, and extract the knowledge they acquire for the benefit of downstream tasks? Motivated to achieve this aim, we present SODA, a self-supervised diffusion model, designed for both perception and synthesis. It couples an image encoder with the classic diffusion decoder \[ 5 \] , both trained in tandem for novel view generation \[ 21 \] — a task we choose to employ here, not only for its own sake, but as a self-supervised objective. This setup introduces a desirable information bottleneck between the encoder and the decoder \[ 22 \] , that in contrast to the typical diffusion framework, equips our model with an explicit and interpretable visual latent space. As our experiments confirm, its advantages are twofold: it both encourages the emergence of disentangled and informative representations that capture image key properties and semantics, which thus can be applied to downstream tasks, and further provides effective means to control and manipulate the produced outputs, for the gain of image editing and synthesis. We further devise and integrate multiple new ideas into the network architecture and training procedure: layer modulation, modified classifier-free guidance, and an inverted noise schedule, so to maximize its representation skills. SODA possesses strong representation skills, attaining high performance in linear-probing experiments over the ImageNet dataset among others. Moreover, it excels at the task of few-shot novel view generation, and can flexibly synthesize images either conditionally or unconditionally, as indicated by metrics of fidelity, consistency and diversity. Overall, SODA integrates together three research ideas that we seek to establish and promote: First, diffusion models are not only adept at image generation, but are also capable of learning strong representations. Second, novel view synthesis can serve as a powerful self-supervised objective for model pre-training. The advent of diffusion models has lately marked a breakthrough in the field of visual synthesis. Originally inspired by theories of thermodynamics \[ 23 \] , it approaches generative modeling by following a reversible and iterative denoising process, the forward direction of which slowly erodes the structure within the data distribution, while the backward direction is gradually restoring it. Consequently, diffusion models have been widely adopted for numerous tasks and modalities \[ 29 , 30 , 31 , 32 , 33 \] , synthesizing images, videos, audio and text \[ 34 , 35 , 36 , 37 , 38 \] , and even advancing planning \[ 39 \] and drug discovery \[ 40 \] , effectively becoming one of the leading paradigms for generative modeling nowadays. The reliance on such models makes it unclear whether the downstream capabilities arise from the diffusion approach itself, or are actually attributable to the exceptionally large scales, long training and voluminous captioned data, which, essentially, provides rich and textual semantic supervision. To address this shortcoming, we focus here instead on the fully-unsupervised regime, and train our model from scratch on standardized benchmarks, seeking to asses the value and potential of diffusion-based representations derived from images alone. Visual Encoding. Closer to our work is DRL \[ 20 \] , that extends early research on denoising auto-encoders \[ 44 , 45 , 46 , 47 \] , and conditions a denoiser on an encoded clean version of its own target. DiffAE \[ 48 \] follows up, integrating style modulation into the encoder \[ 49 , 50 , 51 \] , while InfoDiffusion \[ 52 \] regularizes it with mutual-information loss. Our approach builds upon this line of research, but instead of auto encoding the same image, we generate novel views. We further couple this idea with multiple technical innovations, pertaining both architecture and optimization, geared to realize the representational capabilities of diffusion models to their fullest. And in contrast to prior works, we provide an extensive empirical study of diffusion-based representation learning, encompassing a broad suite of datasets over multiple different tasks. Hybrid Models. Consequently, we note that the latent representations used in both these approaches are in fact not derived by diffusion itself, but rather through either contrastive or adversarial pre-training. As such, they differ fundamentally from our study, which aims to explore the effectiveness of diffusion-based pre-training as a means for representation learning. Downstream Tasks. For each of the tasks we explore — classification, disentanglement, reconstruction, and novel view synthesis — we compare SODA to the leading prior works. These include models such as SimCLR, DINO, and MAE for linear-probe classification \[ 53 , 54 , 55 , 56 , 57 , 58 \] , NeRF-based approaches for novel view generation \[ 59 , 60 \] , and classic variational models for the task of disentanglement \[ 61 , 62 , 63 \]. Whereas these techniques are designed for particular objectives or depend on domain-specific assumptions, SODA exhibits a greater degree of versatility, as it tackles representational and generative tasks alike. SODA is a self-supervised diffusion model that learns a bidirectional mapping between images and latents. This design equips SODA with an explicit and compact latent space, which not only offers ample control over the generative process, but can also be leveraged for downstream perception tasks Section 4 1 1 1 Our model is named after the soda drink. Indeed, the fizzing in soda bottles is an everyday example of the diffusion phenomena. We first present an overview of the model Section 3. See Appendix B for the architectural details of the decoder, as well as closed-form equations of the diffusion forward and backward steps. This endows SODA with finer control over the generative process, and opens the door for image editing and style mixing \[ 49 \] , as we can selectively condition the decoder on some levels of granularity, like structural or positional aspects, while giving it free rein to unconditionally vary other ones, such as lighting, texture, or color palette see supplementary figures. We loosely consider views to be any set of images that hold some relation among each other, such as visual or semantic Figure 2 : they can be various augmentations or distortions of an original image, as is commonly explored in the contrastive learning literature \[ 58 \] , they can show a 3D object from different poses and perspectives \[ 21 \] , or they can simply share the same semantic category with one another. We permit the trivial singular case where all views are identical, which then turns the model into an auto-encoder. The model can incorporate richer forms of conditional information, such as the camera perspective associated with each view: Specifically, for experiments over 3D datasets like ShapeNet Section 4. This allows us to conditionally generate novel views that match the requested pose and orientation. See supplementary for illustrations and implementation details. Cross Attention. We further study alternative mechanisms, and explore the integration of cross attention, so to support spatial modulation. We find that cross attention aids the model at 3D novel view synthesis, while layer modulation performs better for image editing, reconstruction, and representation learning. Noise Schedule. Those schedules have been found useful for image synthesis. We thus incorporate a new inverted noise schedule, that promotes medium noise levels in lieu of the extremes, which proves highly conducive to representation quality Section 4. Additional Settings. While the model is robust to the selection of the learning-rate ratio, tuning it could improve downstream results. Once trained, we use DDPM \[ 5 \] for sampling. We evaluate SODA through a suite of quantitative and qualitative experiments, demonstrating its strong representation skills and generative capabilities over 12 different datasets grouped into 4 tasks: We begin with linear-probe classification Section 4. We proceed to image reconstruction and few-shot novel view synthesis Section 4. In the supplementary and our website website soda-diffusion. Taken altogether, the evaluation offers solid evidence for the efficacy, robustness and versatility of our approach. We note that training diffusion models for representation learning is computationally efficient, since iterative sampling is necessary for generative purposes only. Meanwhile, for CelebA, our model attains the strongest results SODA proves remarkably robust to the choice of data augmentation , as it performs strongly regardless of the selected strategy, seeing only a minor decrease of 3. This stands in stark contrast to the high sensitivity of contrastive methods to data augmentations, with e. Other contributors include the compact bottleneck and feature modulation , which respectively raise accuracy by For the task of few-shot novel view synthesis, we focus on the 3D regime and look into 3 datasets that span both synthetic object renderings and real-world scans of household items Google Scanned Objects \[ 86 \] , custom ShapeNet \[ 87 \] , and NMR \[ 88 \]. We condition the models on source views, and test them on held-out validation objects that do not appear in training. It reaches the largest gains along LPIPS and FID, producing significantly sharper images that better match the source views both structurally and semantically. We observe that settings of source views benefit the most from our model, where for the single-source case, it improves FID scores by an order-of-magnitude and often almost halves the LPIPS scores. Yet, in terms of computational efficiency, contrary to the slow and heavy rendering of geometry-aware methods, SODA maintains strong performance with as little as 20 sampling steps. Figure 5 and the supplementary animations feature objects synthesized from various perspectives, showcasing the viewpoint consistency SODA achieves. For multiple sources, we find that our proposed transformer-based view aggregation Section 3. Our approach further outperforms the denoiser-only Palette diffusion model, which fits translational tasks that closely follow the source layout, like colorization or super-resolution, but struggles at structural transformations, corroborating the need for our dedicated image encoder. The concept of disentanglement has been a recurring theme in representation learning research over the years \[ 90 , 91 , 92 , 93 , 94 \]. While formal definitions may vary \[ 95 , 96 , 97 , 98 , 99 \] , a common aim lies in the discovery of abstract and meaningful latent representations that linearly align with the natural axes of variation. Latent Interpolations. We observe smooth variations over traits of texture and structure. Notable in particular are image categories that seamlessly morph from one to another e. Attribute Manipulation. We go beyond interpolations and identify meaningful latent directions that correspond to individual axes of variation. Layer Modulation. We investigate the effect of layer modulation and masking Section 3. As illustrated in the supplementary, it allows for selective modification of input images at different levels of granularity, so to preserve certain factors while unconditionally regenerating other ones. To quantitatively bolster the findings above, we analyze our approach with DCI \[ 94 \] , which measures representations along Disentanglement, Completeness and Informativeness by assessing the degree of 1-to-1 correspondence between latent and ground-truth factors of variation Appendix E. As Table 4 and supplementary Tables 6 and 7 show, SODA outshines both variational and adversarial approaches, improving Disentanglement by Our experiments further validate the contribution of layer modulation and masking, respectively yielding 3. We introduced SODA, a self-supervised diffusion model, designed for both perception and synthesis. It re-purposes the task of novel view generation as a training objective for representation learning. By conditioning a denoiser on an image encoder, and imposing an information bottleneck between the two, SODA learns strong semantic representations that enable downstream classification, as well as reconstruction, editing and synthesis. While we focused on single-object images, as in LSUN, ShapeNet, or ImageNet, we believe that exploring the applicability of our approach to dynamic compositional scenes is a promising direction for future research. We hope our work will help bridging the gap between novel view synthesis and self-supervised learning, two flourishing topics that are often pursued independently, and bring us one step closer to unlocking the potential of generative models in general and diffusion models in particular to advance the representational frontier. In the following, we discuss additional analysis of our approach, and provide further description of the model structure, implementation details, and evaluation procedures. In Appendix C , we then specify the chosen hyperparameters, training techniques, and sampling methods. Appendices D , E and F respectively review the datasets, metrics, and baselines we consider in this study. Finally, in Appendix G , we present ablation and variation studies that assess the contribution of each of our design choices, complementing the principal ones explored in the main paper. We plan very soon to add to the supplementary and our website soda-diffusion. Thanks to the reparametrization trick \[ 5 \] , we can then sample the following:. See Table 12 for our chosen hyperparameters. For each dataset, we train the model until convergence, as measured by lack of improvement over a set number of training steps along a validation metric of choice either downstream accuracy or SSIM. For sampling, we use discrete-time DDPM \[ 5 \] , classifier-free guidance \[ 27 \] and diffusion timestemps, practically strided into steps \[ 69 \]. Positional Encoding. Pose Conditioning. Throughout the paper, we experiment with several different flavors of the novel view synthesis task: either generating a view conditionally, matching a 3D pose or 2D coordinates, or alternatively, in a pose-unconditional fashion: where given a source view, the model is asked to generate arbitrary novel views at perspectives of its choice. In Appendix G , we compare different ways to represent the rays, such as through normalization, by casting them on a plane or a sphere, or by summing up their positions and directions. Learning Rates. While the model is robust to the selection of the learning rate ratio, we find that a ratio of 2 yields optimal downstream results Appendix G. Throughout this work, we evaluate models over various datasets grouped into multiple tasks, as summarized by Table 10 and through the textual description below:. Imagenet1K \[ \] : includes diverse images of objects among 1, categories of e. CelebA-HQ \[ \] : features face images, annotated with 40 binary semantic properties like age, gender, or hair color; used also for quantitative disentanglement analysis. LSUN \[ \] : partitioned into multiple categories of objects like cars, cats and horses and scenes e. Oxford Flowers \[ \] : features diverse flowers from the United Kingdom. We use the SoftRas data split \[ \]. Google Scanned Objects GSO \[ 86 \] : includes scans of real-world household items, which we render with Blender following the same protocol described above. Disentanglement Quantitative : Each image in the following datasets is associated with discrete semantic attribute annotations. SmallNORB \[ \] : contains toy images belonging to 5 categories like animals and vehicles, captured from various camera perspectives and lighting conditions. Indeed, this technique has been shown to improve the overall sample quality, and could readily fit with our approach as well. Data Splits. Data is shuffled at training time. We note that for all the multi-view datasets: NMR, ShapeNet, GSO, and smallNorb, we intentionally keep all the views of each object exclusively grouped within one of the splits, and consequently, all the objects used for evaluation are not included in the training set. Specifically, at every training step, we randomly augment each view, at the rates specified in Table To train the subsequent downstream classifier, we perform cropping and flipping only, and finally, at evaluation time, perform only center-cropping, following the standard linear probing protocols of prior self-supervision learning works \[ 56 , 53 \]. When training the diffusion model, we also find it conducive to add low Gaussian noise to the encoded source view, similarly to the noise added to the denoised target view. Meanwhile, for multi-view 3D datasets such as NMR, GSO and ShapeNet, we do not apply data augmentations, and instead, randomly sample one view as the source and another as the target, further supplied by their respective camera perspectives Appendix C. Lastly, to illustrate the ability of SODA to learn useful representations even without relying on data augmentation, we perform ablations on datasets used as is, forgoing augmentations of any kind. In Section 4. When training the classifier, we refrain from applying weight decay, and adhere to either light augmentation of cropping and flipping for ImageNet or no augmentation in other cases. After normalizing the latents, we use 0. Since the annotated datasets we explore all have discrete labels, we use softmax cross entropy to train the classifier, and report its performance along metrics such as F1 for binary attributes, and top1 accuracy for other ones. It may rate a blurry estimation as highly consistent with the target, as long as they match well with each other on average. It concretely achieves it by considering the mean and variance of each, in a latent feature space, e. When assessing unconditionally-generated images, the FID score further expresses their diversity, but in the case of conditional synthesis, either as reconstructions or with pose conditioning, it mainly reflects their fidelity, sharpness and lack of distortions also known as R-FID in this context. These metrics are derived from the normalized importance matrix of a learned classifier and its performance, where the classifier is based on either gradient boosting or Lasso we use the former. Our implementation of these metrics closely follows Locatello et al. Thanks to layer modulation Section 3. Indeed, these probes are trained to capture the latent directions that correspond to the presence or absence of the semantic attribute annotations that accompany the datasets we study. The key difference between the PCA-based approach and this technique is that the former is unsupervised while the latter is not. For each of the tasks we explore, we compare our model to the respective leading approaches, as well as to additional ablated baselines that we design. Here, we list and review all the baseline methods we compare to. First, we implement multiple baselines and ablated models within our diffusion codebase, and report their performance across the range of tasks:. We emphasize that we do not refer here to the already trained Dall-E2 model, but rather to its architecture, and so we train its denoiser in a comparable size to our model from scratch along with the frozen pre-trained CLIP encoder, for each dataset of interest. For downstream classification, we compare our model to a diverse array of leading self-supervised learning approaches: generative methods like MAE \[ 53 \] , BEIT \[ 54 \] and iGPT \[ 55 \] split each image into a grid of tokens or patches, mask some patches and predict them back from the unmasked ones, oftentimes using a transformer backbone. At the core of these methods is a strong reliance on rich data augmentations, which are essentially the driving force that allows the to perform unsupervised clustering. The semantic properties they may or may not encode into the learned representations heavily depend on the particularities of the data augmentation scheme they employ, since they are basically encouraged to form a latent space that is invariant to the augmentation applied, instead casting different augmentations into similar representations. Contrary to these two kinds of approaches, both of which are unsuitable for high-quality image generation, SODA stands out being able to both encode input images into meaningful latents, and also synthesize back crisp output images, conditionally and unconditionally. It learns compact and disentangled representations, which contrast with the large, potentially discrete, 2D grids learned by alternative approaches, and as demonstrated in Section 4. We examine the performance of varied models for the task of image reconstruction: Dall-E \[ 74 \] and VQGAN \[ 75 \] employ a discrete variational auto-encoder \[ \] , which casts input images into 2D token grids, based on a trainable codebook. These approaches then couple the auto-encoder with a prior-distribution model, to enable unconditional image synthesis. However, for our purposes image reconstruction , we consider the auto-encoder module only. The adversarial StyleGAN model \[ 49 \] can also be used for image reconstruction, by applying optimization-based inversion techniques to infer back latents from images. While these techniques tend to produce samples that share semantic properties with the source images, they oftentimes fail to reconstruct them faithfully. Finally, we compare our model to the diffusion-based DiffAE \[ 48 \] , which, in contrast to our study, focuses on auto-encoding only, and can be viewed as a predecessor of our approach, as discussed in Section 2. We assess the reconstruction capabilities of the approaches described in this subsection by evaluating a sample set of images produced by their associated public pre-trained checkpointed models. For novel view synthesis of 3D objects, we compare SODA to a collection of geometry-free and -aware approaches designed for few-shot settings: PixelNeRF \[ 60 \] learns to translate a small number of source views into a neural radiance field, and then use volumetric rendering techniques to generate new ones. NeRF-VAE \[ 59 \] extends this idea by leveraging amortized variational inference to learn probablistic neural scene representations. In contrast to these specialized methods, designed specifically for 3D environments, SODA proves considerably more versatile, successfully addressing a broader spectrum of tasks and datasets. In contrast to these approaches, we intentionally introduce a bottleneck into our model that induces a meaningful and compact latent space. We evaluate these methods using the official disentanglement-lib TensorFlow repository \[ 61 \] , while modifying the backbone encoder and decoder architectures to match the ones used in SODA, for better comparability. This study joins ablations presented through the main paper Sections 4. We explore multiple modulation variants and examine how they fare in terms of generative skills and downstream performance Tables 9 , 11 and 7. Layer modulation proves beneficial too, enhancing disentanglement scores, with up to Drew A. Google DeepMind. Source View. Noise Scale. Imagenet1K \[ \]. CelebA-HQ \[ \]. Gaussian Noise. LSUN \[ \]. AFHQ \[ \]. NMR \[ 88 \]. ShapeNet \[ 87 \]. GSO \[ 86 \]. SmallNORB \[ \]. MPI3D Toy \[ \]. MPI3D Realistic \[ \]. MPI3D Real \[ \]. MPI3D Complex \[ \]. Oxford Flowers \[ \]. Cosine Decay. Attention Heads Number. Hidden Layer Multiplier. Channels multipliers. Residual blocks per resolution. Selt-Attention resolution. Attention Head Dimension. Normalization Type. GroupNorm \[ 51 \]. Dropout Rate. Diffusion Training Steps. Linear Probe. Weight Initialization Scale. Augmentation Rate RandomResizedCrop. Data Augmentation. Distortation Rate RandAugment \[ 79 \]. Distortion Layers Number. Distortion Magnitude. Distortion Magnitude STD.
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