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Found 9 papers, 5 papers with code
Physics-based Human Motion Estimation and Synthesis from Videos
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.
UniCon: Universal Neural Controller For Physics-based Character Motion
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
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.
Benchmarking Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL.
Exploring Model-based Planning with Policy Networks
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.
Neural Graph Evolution: Towards Efficient Automatic Robot Design
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
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
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.
NerveNet: Learning Structured Policy with Graph Neural Networks
We address the problem of learning structured policies for continuous control.
