发布日期: 2025-09-20

更新日期: 2025-09-22

文章字数: 994

阅读时长: 4 分

阅读次数:

⚠️ 以下所有内容总结都来自于大语言模型的能力，如有错误，仅供参考，谨慎使用
🔴 请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！
💗 如果您觉得我们的项目对您有帮助 ChatPaperFree ，还请您给我们一些鼓励！⭐️ HuggingFace免费体验

2025-09-20 更新

Morph: A Motion-free Physics Optimization Framework for Human Motion Generation

Authors:Zhuo Li, Mingshuang Luo, Ruibing Hou, Xin Zhao, Hao Liu, Hong Chang, Zimo Liu, Chen Li

Human motion generation has been widely studied due to its crucial role in areas such as digital humans and humanoid robot control. However, many current motion generation approaches disregard physics constraints, frequently resulting in physically implausible motions with pronounced artifacts such as floating and foot sliding. Meanwhile, training an effective motion physics optimizer with noisy motion data remains largely unexplored. In this paper, we propose \textbf{Morph}, a \textbf{Mo}tion-F\textbf{r}ee \textbf{ph}ysics optimization framework, consisting of a Motion Generator and a Motion Physics Refinement module, for enhancing physical plausibility without relying on expensive real-world motion data. Specifically, the motion generator is responsible for providing large-scale synthetic, noisy motion data, while the motion physics refinement module utilizes these synthetic data to learn a motion imitator within a physics simulator, enforcing physical constraints to project the noisy motions into a physically-plausible space. Additionally, we introduce a prior reward module to enhance the stability of the physics optimization process and generate smoother and more stable motions. These physically refined motions are then used to fine-tune the motion generator, further enhancing its capability. This collaborative training paradigm enables mutual enhancement between the motion generator and the motion physics refinement module, significantly improving practicality and robustness in real-world applications. Experiments on both text-to-motion and music-to-dance generation tasks demonstrate that our framework achieves state-of-the-art motion quality while improving physical plausibility drastically.

人类动作生成的研究在数字人类和仿人机器人控制等领域中扮演了关键角色，因此受到了广泛关注。然而，许多现有的动作生成方法忽略了物理约束，经常导致出现明显的物理不真实动作，如漂浮和脚滑等。同时，利用噪声动作数据训练有效的运动物理优化器仍鲜有研究。

论文及项目相关链接

PDF Accepted by ICCV 2025, 15 pages, 6 figures

Summary：

本文提出了一种名为Morph的运动物理优化框架，包含运动生成器和运动物理细化模块，旨在提高物理合理性，且不依赖昂贵的真实运动数据。运动生成器提供大规模合成噪声运动数据，而运动物理细化模块则利用这些数据在物理模拟器内学习运动模仿者，执行物理约束将噪声运动投影至物理可行空间。此外，引入先验奖励模块以增强物理优化过程的稳定性，生成更平滑、更稳定的运动。经物理精化的运动用于微调运动生成器，进一步提高其能力。此协作训练范式使运动生成器和运动物理细化模块相互增强，在真实世界应用中显著提高实用性和稳健性。

Key Takeaways：