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.
no code implementations • 30 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.
no code implementations • 18 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.
2 code implementations • 3 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.
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.
1 code implementation • 12 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.
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.
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.
1 code implementation • ICLR 2018 • Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler
We address the problem of learning structured policies for continuous control.