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          Disney Combines CGI With Neural Rendering to tackle the ‘Uncanny
          Valley’
       
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          [98] Image Synthesis
       
          Disney Combines CGI With Neural Rendering to tackle the ‘Uncanny
          Valley’
          ================================================================
       
          mm
       
          Published
       
          8 seconds ago
       
          on
       
          November 30, 2021
       
          By
       
          [99]Martin Anderson
       
          Table Of Contents
       
          Disney’s AI research division has developed a hybrid method for
          movie-quality facial simulation, combining the strengths of facial
          neural rendering with the consistency of a CGI-based approach.
       
          The pending paper is titled Rendering with Style: Combining
          Traditional and Neural Approaches for High Quality Face Rendering, and
          is previewed in a [100]new 10-minute video at the Disney Research
          YouTube channel (embedded at end of this article).
       
          Meshes combined with neural facial renders. Source: https://www.youtube.com/watch?v=k-RKSGbWLng
       
          Meshes combined with neural facial renders. See video embed at end of
          article for better detail and quality. Source:
          https://www.youtube.com/watch?v=k-RKSGbWLng
       
          As the video notes, [101]neural rendering of faces (including [38]deepfakes)
          can produce far more realistic eyes and mouth interiors than CGI is
          capable of, while CGI-driven facial textures are more consistent and
          suitable for cinema-level VFX output.
       
          Therefore Disney is experimenting with letting NVIDIA’s [102]StyleGan2
          neural generator handle the surrounding features of a face and the
          ‘life-critical’ elements such as eyes, while superimposing consistent
          CGI facial skin and related elements into the output.
       
          From the video (see end of article), the architectural concept behind Disney's hybrid approach, where an old-school CGI mesh, of the type used to recreate 'young' Carrie Fisher and the late Peter Cushing for Rogue One (2016), is integrated into neurally-rendered face environments.From the video (see end of article), the architectural concept behind Disney's hybrid approach, where an old-school CGI mesh, of the type used to recreate 'young' Carrie Fisher and the late Peter Cushing for Rogue One (2016), is integrated into neurally-rendered face environments.
       
          From the video (see end of article), the architectural concept behind
          Disney’s hybrid approach, where an old-school CGI mesh, of the type
          used to recreate ‘young’ Carrie Fisher and the late Peter Cushing for
          Rogue One (2016), is integrated into neurally-rendered face
          environments.
       
          The video makes a tacit reference to [103]frequent criticism of the
          inauthenticity and ‘uncanny valley’ effect of the CGI recreation of
          late British Star Wars actor Peter Cushing in Rogue One (2016),
          conceding:
       
          ‘[There’s] still a huge gap between what people can easily capture and
          render versus final photorealistic digital doubles, complete with
          hair, eyes and inner mouth. To close this gap, it usually takes a lot
          of manual work from skilled artists.’
       
          In truth, even the most modern facial capture systems do not even
          attempt to recreate eyes, mouth interiors or hair, which either have
          issues of authenticity in such techniques (eyes) or else of temporal
          consistency (hair).
       
          The video illustrates what VFX artists will get after a typical modern facial capture session. Eyes, hair, facial hair, and mouth interiors will all have to be handled by separate teams in the production pipeline.The video illustrates what VFX artists will get after a typical modern facial capture session. Eyes, hair, facial hair, and mouth interiors will all have to be handled by separate teams in the production pipeline.
       
          The video illustrates what VFX artists will get after a typical modern
          facial capture session. Eyes, hair, facial hair, and mouth interiors
          will all have to be handled by separate teams in the production
          pipeline, in addition to texturing and lighting.
       
          Illumination Control
       
          The hybrid approach is also a benefit with relighting – a notable
          challenge for neural rendering of faces, since CGI skin
          superimpositions can be more easily relit.
       
          An animated version of the CGI/Neural approach.
       
          An animated version of the CGI/Neural approach.
       
          In more challenging environments, such as exterior shoots, the
          researchers have developed a method of inpainting around a kind of
          demilitarized zone surrounding the person being ‘created’.
       
          A black margin is generated to allow a 'canvas' for inpainting the outer parts of the identity and integrating the CGI skin into the combined CGI/neural output.A black margin is generated to allow a 'canvas' for inpainting the outer parts of the identity and integrating the CGI skin into the combined CGI/neural output.
       
          A black margin is generated to allow a ‘canvas’ for inpainting the
          outer parts of the identity and integrating the CGI skin into the
          combined CGI/neural output.
       
          The video notes:
       
          ‘[The] neural render does not match the background constraint
          perfectly. – it’s only meant as a guide, since optimizing for
          realistic human components like the hair, eyes and teeth is the main
          goal. More challenging is to try and maintain a consistent identity,
          while changing the environment lighting.’
       
          Creating CGI Meshes From Neural Renders
       
          The research team have also developed a variational autoencoder
          trained on a (unspecified) large database of 3D face images, and
          claims that it can produce ‘random but plausible’ 3D face meshes from
          ground truth data.
       
          There are limitations for this research to overcome, including the
          difficulty in getting hair to stay temporally consistent in the neural
          renderings, and the video (see below) shows several examples of
          rapidly mutating hair in an otherwise consistent pan around a
          CGI/neural face.
       
          Temporal consistency in neural video rendering is a far wider problem
          than just Disney’s, and it seems likely that later iterations of this
          system may resort to adding hair ‘in post’, or various other possible
          approaches to hair generation than hoping a novel neural approach will
          eventually solve it.
       
          Uses for Dataset Generation
       
          The method is proposed also as a potential method of generating [68]synthetic
          data, and enriching the facial image set landscape, which has in
          recent years become [104]dangerously monotonous.
       
          Disney envisages the new technique populating facial image datasets.Disney envisages the new technique populating facial image datasets.
       
          Disney envisages the new technique populating facial image datasets.
       
          ‘[Every] photorealistic result we generate has an underlying
          corresponding geometry, and appearance maps, rendered from unknown
          camera viewpoints with known illumination. This ‘ground truth’
          information can be vital for training downstream applications, such as
          monocular, 3D face reconstruction, facial recognition, or scene
          understanding. And so every results render could be considered a data
          sample, and we can generate many variations of many different
          individuals.
       
          ‘Furthermore, even for a single person rendered in a single expression
          with a single viewpoint and illumination, we can generate random
          variations of the photo-real render by varying the randomization seed
          during optimization.’
       
          The researchers note that this diversity of configurable output could
          be useful in training facial recognition applications, concluding:
       
          ‘[Our] method is able to leverage current technology for facial skin
          capture, modeling and rendering, and automatically create complete
          photorealistic face renders that match the desired identity,
          expression and scene configuration. This approach has applications and
          facial rendering for film and entertainment, saving manual artists
          labor and also for data generation in different fields of [39]deep
          learning.’
       
          For a deeper look at the new approach, check out the 10-minute video
          released today:
       
          Rendering with Style Combining Traditional and Neural Approaches for
          High Quality Face Rendering[105]Rendering with Style Combining Traditional and Neural Approaches for High Quality Face Rendering
          Watch this video on YouTube
       
          Related Topics:[106]image synthesis[107]research[108]synthetic data[109]visual
          AI[110]Don't Miss
       
          Neural Rendering: NeRF Takes a Walk in the Fresh Air
       
          mmmm[99]Martin Anderson[111] [112]
       
          Journalist and editor, primarily on machine learning, artificial
          intelligence and big data. Personal site: https://martinanderson.ai/
          Contact: martin@martinanderson.ai Twitter: @manders_ai
       
          [IMAGE][IMAGE]
       
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