Search Results for author: Tomas Simon

Found 21 papers, 8 papers with code

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.

Driving-Signal Aware Full-Body Avatars

no code implementations21 May 2021 Timur Bagautdinov, Chenglei Wu, Tomas Simon, Fabian Prada, Takaaki Shiratori, Shih-En Wei, Weipeng Xu, Yaser Sheikh, Jason Saragih

The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.

Imputation

Pixel Codec Avatars

no code implementations CVPR 2021 Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando de la Torre, Yaser Sheikh

Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances.

SimPoE: Simulated Character Control for 3D Human Pose Estimation

no code implementations CVPR 2021 Ye Yuan, Shih-En Wei, Tomas Simon, Kris Kitani, Jason Saragih

Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e. g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame.

3D Human Pose Estimation Frame

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

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

3 code implementations CVPR 2020 Shunsuke Saito, Tomas Simon, Jason Saragih, Hanbyul Joo

Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.

3D Human Pose Estimation 3D Human Reconstruction +3

Single-Network Whole-Body Pose Estimation

2 code implementations ICCV 2019 Gines Hidalgo, Yaadhav Raaj, Haroon Idrees, Donglai Xiang, Hanbyul Joo, Tomas Simon, Yaser Sheikh

We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints.

Multi-Task Learning Pose Estimation

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.

Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction

1 code implementation CVPR 2019 Hanbyul Joo, Tomas Simon, Mina Cikara, Yaser Sheikh

We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of social signals humans use.

LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds

no code implementations CVPR 2019 Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos, Yaser Sheikh

As input, we take a sequence of point clouds to be registered as well as an artist-rigged mesh, i. e. a template mesh equipped with a linear-blend skinning (LBS) deformation space parameterized by a skeleton hierarchy.

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.

Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

no code implementations CVPR 2018 Hanbyul Joo, Tomas Simon, Yaser Sheikh

We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures.

Panoptic Studio: A Massively Multiview System for Social Interaction Capture

1 code implementation9 Dec 2016 Hanbyul Joo, Tomas Simon, Xulong Li, Hao liu, Lei Tan, Lin Gui, Sean Banerjee, Timothy Godisart, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, Yaser Sheikh

The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.

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