ポリシーをデータとして：シミュレーション物理から一般化可能なHOI拡散モデルを学習する
Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics
物理シミュレータで強化学習により訓練したポリシーを用いてタスク指向のデータを生成し、そのデータで拡散モデルを訓練することで、未見物体への一般化と長期的な物理整合性を持つ人間-物体インタラクション生成を実現する。
著者: Shujia Li, Jianshu Hu, Haiyu Zhang, Yunpeng Jiang, Haoyuan Jin, Xinyuan Chen, Yaohui Wang, Yutong Ban
分類: cs.CV
原文アブストラクト
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primarily rely on motion capture datasets, which are expensive to scale and limited in functional diversity. Models trained with these datasets fail to generalize to unseen objects and maintain physical consistency over long horizons. In this paper, we propose a novel framework that leverages a physics simulator to overcome the data-scarcity bottleneck in HOI generation. Specifically, we propose a scalable pipeline, called \ours, which leverages policies trained with reinforcement learning in a physics simulator for task-oriented data generation and trains a generative model on the augmented dataset for generalizable HOI generation. To seamlessly utilize the synthetic data, we introduce a coarse-to-fine retargeting process that bridges the representation gap between the simplified model used in physics simulator and the standard parametric body models required for generative training. Validated through comprehensive experiments, our method demonstrates enhanced generalization to unseen objects and the capability of long-horizon generation, while exhibiting greater dynamic diversity and physical plausibility.