Search Results for author: Yunrong Guo

Found 6 papers, 1 papers with code

Factory: Fast Contact for Robotic Assembly

no code implementations7 May 2022 Yashraj Narang, Kier Storey, Iretiayo Akinola, Miles Macklin, Philipp Reist, Lukasz Wawrzyniak, Yunrong Guo, Adam Moravanszky, Gavriel State, Michelle Lu, Ankur Handa, Dieter Fox

We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics.

ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters

no code implementations4 May 2022 Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, Sanja Fidler

By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills.

Imitation Learning Unsupervised Reinforcement Learning

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

KAMA: 3D Keypoint Aware Body Mesh Articulation

no code implementations27 Apr 2021 Umar Iqbal, Kevin Xie, Yunrong Guo, Jan Kautz, Pavlo Molchanov

We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints.

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.

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