仅用短暂走路数据，生成逼真且多样的抓取动作。
Learning Physics-Based Full-Body Human Reaching and Grasping from Brief Walking References
- 用走路数据提取运动模式，指导抓取动作生成。
- 生成动作成功率高，自然度强，适配新场景和物体。
- 适合机器人动作规划、虚拟角色动画等应用。
现有基于动作捕捉（mocap）数据的运动生成方法常受限于数据质量和覆盖范围。本文提出一种框架，仅需简短的行走动作捕捉数据，即可生成多样且物理可行的全身人形伸手抓取动作。基于观察：行走数据蕴含可迁移的运动模式；而先进运动学方法能生成多样抓取姿态，可插值为任务特定引导。本方法采用主动数据生成策略以最大化生成动作的效用，并引入局部特征对齐机制，将行走数据中的自然运动模式迁移至生成动作中，从而提升合成动作的成功率与自然度。结合自然行走的保真度与稳定性，以及任务导向数据的灵活性与泛化能力，该方法在多种场景及未见物体上均表现出强大性能与鲁棒适应性。
原文摘要 · Abstract (English)
Existing motion generation methods based on mocap data are often limited by data quality and coverage. In this work, we propose a framework that generates diverse, physically feasible full-body human reaching and grasping motions using only brief walking mocap data. Base on the observation that walking data captures valuable movement patterns transferable across tasks and, on the other hand, the advanced kinematic methods can generate diverse grasping poses, which can then be interpolated into motions to serve as task-specific guidance. Our approach incorporates an active data generation strategy to maximize the utility of the generated motions, along with a local feature alignment mechanism that transfers natural movement patterns from walking data to enhance both the success rate and naturalness of the synthesized motions. By combining the fidelity and stability of natural walking with the flexibility and generalizability of task-specific generated data, our method demonstrates strong performance and robust adaptability in diverse scenes and with unseen objects.
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