Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhuo Li, Zhuo Li, , ,
| Summary:
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 textbfMorph, a textbfMotion-Ftextbfree textbfphysics
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.
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