Search Results for author: Stephen Lombardi

Found 16 papers, 6 papers with code

AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation

no code implementations30 May 2023 Thu Nguyen-Phuoc, Gabriel Schwartz, Yuting Ye, Stephen Lombardi, Lei Xiao

Among existing approaches for avatar stylization, direct optimization methods can produce excellent results for arbitrary styles but they are unpleasantly slow.


MEGANE: Morphable Eyeglass and Avatar Network

no code implementations CVPR 2023 Junxuan Li, Shunsuke Saito, Tomas Simon, Stephen Lombardi, Hongdong Li, Jason Saragih

However, modeling the geometric and appearance interactions of glasses and the face of virtual representations of humans is challenging.

Image Generation Inverse Rendering

RelightableHands: Efficient Neural Relighting of Articulated Hand Models

no code implementations CVPR 2023 Shun Iwase, Shunsuke Saito, Tomas Simon, Stephen Lombardi, Timur Bagautdinov, Rohan Joshi, Fabian Prada, Takaaki Shiratori, Yaser Sheikh, Jason Saragih

To achieve generalization, we condition the student model with physics-inspired illumination features such as visibility, diffuse shading, and specular reflections computed on a coarse proxy geometry, maintaining a small computational overhead.

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.

Generalizable Novel View Synthesis

Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality

no code implementations CVPR 2022 Amin Jourabloo, Baris Gecer, 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

1 code implementation2 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.

Learning Compositional Radiance Fields of Dynamic Human Heads

1 code implementation CVPR 2021 Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, Michael Zollhöfer

In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code.

Neural Rendering Synthetic Data Generation

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.

BIG-bench Machine Learning Image Generation +2

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

1 code implementation1 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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