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Found 11 papers, 2 papers with code
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture
Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars.
Advances in Neural Rendering
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
Pixel-Aligned Volumetric Avatars
Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.
Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality
Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR).
Mixture of Volumetric Primitives for Efficient Neural Rendering
Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications.
PVA: Pixel-aligned Volumetric Avatars
Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.
Learning Compositional Radiance Fields of Dynamic Human Heads
In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code.
State of the Art on Neural Rendering
Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.
Neural Volumes: Learning Dynamic Renderable Volumes from Images
Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion.
Deep Appearance Models for Face Rendering
At inference time, we condition the decoding network on the viewpoint of the camera in order to generate the appropriate texture for rendering.
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images
Recovering the radiometric properties of a scene (i. e., the reflectance, illumination, and geometry) is a long-sought ability of computer vision that can provide invaluable information for a wide range of applications.
