Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

Author:

Chao Yu-Wei,Yang Jimei,Chen Weifeng,Deng Jia

Abstract

Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. To bridge this gap, we focus on one class of interactive tasks---sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A supplementary video can be found at https://youtu.be/3CeN0OGz2cA.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Synthesizing Physically Plausible Human Motions in 3D Scenes;2024 International Conference on 3D Vision (3DV);2024-03-18

2. GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency;2024 International Conference on 3D Vision (3DV);2024-03-18

3. Physically Plausible Full-Body Hand-Object Interaction Synthesis;2024 International Conference on 3D Vision (3DV);2024-03-18

4. ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation;2024 International Conference on 3D Vision (3DV);2024-03-18

5. Object Motion Guided Human Motion Synthesis;ACM Transactions on Graphics;2023-12-05

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