Search Results for author: Tingwu Wang

Found 9 papers, 5 papers with code

Physics-based Human Motion Estimation and Synthesis from Videos

no code implementations ICCV 2021 Kevin Xie, Tingwu Wang, Umar Iqbal, Yunrong Guo, Sanja Fidler, Florian Shkurti

We demonstrate both qualitatively and quantitatively significantly improved motion estimation, synthesis quality and physical plausibility achieved by our method on the large scale Human3. 6m dataset \cite{h36m_pami} as compared to prior kinematic and physics-based methods.

Motion Estimation motion synthesis +1

UniCon: Universal Neural Controller For Physics-based Character Motion

no code implementations30 Nov 2020 Tingwu Wang, Yunrong Guo, Maria Shugrina, Sanja Fidler

The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control from (human) demonstrations.

Learning to Generate Diverse Dance Motions with Transformer

no code implementations18 Aug 2020 Jiaman Li, Yihang Yin, Hang Chu, Yi Zhou, Tingwu Wang, Sanja Fidler, Hao Li

We also introduce new evaluation metrics for the quality of synthesized dance motions, and demonstrate that our system can outperform state-of-the-art methods.

motion synthesis

Benchmarking Model-Based Reinforcement Learning

2 code implementations3 Jul 2019 Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba

Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL.

Model-based Reinforcement Learning reinforcement-learning

Exploring Model-based Planning with Policy Networks

1 code implementation ICLR 2020 Tingwu Wang, Jimmy Ba

Model-based reinforcement learning (MBRL) with model-predictive control or online planning has shown great potential for locomotion control tasks in terms of both sample efficiency and asymptotic performance.

Model-based Reinforcement Learning

Neural Graph Evolution: Towards Efficient Automatic Robot Design

1 code implementation12 Jun 2019 Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba

To address the two challenges, we formulate automatic robot design as a graph search problem and perform evolution search in graph space.

Neural Graph Evolution: Automatic Robot Design

no code implementations ICLR 2019 Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba

To address the two challenges, we formulate automatic robot design as a graph search problem and perform evolution search in graph space.

VirtualHome: Simulating Household Activities via Programs

3 code implementations CVPR 2018 Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, Antonio Torralba

We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive" an artificial agent to execute tasks in a simulated household environment.

Video Understanding

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