Search Results for author: Stephen Lombardi

Found 11 papers, 2 papers with code

HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

no code implementations CVPR 2022 Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhoefer, Jessica Hodgins, Christoph Lassner

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.

Neural Rendering Optical Flow Estimation

Pixel-Aligned Volumetric Avatars

no code implementations CVPR 2021 Amit Raj, Michael Zollhofer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi

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

no code implementations CVPR 2022 Amin Jourabloo, Fernando de la Torre, Jason Saragih, Shih-En Wei, Te-Li Wang, Stephen Lombardi, Danielle Belko, Autumn Trimble, Hernan Badino

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

no code implementations2 Mar 2021 Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, Jason Saragih

Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications.

Neural Rendering

PVA: Pixel-aligned Volumetric Avatars

no code implementations7 Jan 2021 Amit Raj, Michael Zollhoefer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi

Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.

State of the Art on Neural Rendering

no code implementations8 Apr 2020 Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer

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.

Image Generation Neural Rendering +1

Neural Volumes: Learning Dynamic Renderable Volumes from Images

1 code implementation18 Jun 2019 Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh

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

no code implementations1 Aug 2018 Stephen Lombardi, Jason Saragih, Tomas Simon, Yaser Sheikh

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

no code implementations5 Apr 2016 Stephen Lombardi, Ko Nishino

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.

Scene Understanding

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