Towards Synthesized and Editable Motion In-Betweening Through Part-Wise Phase Representation
This work addresses the need for more nuanced and expressive animations in computer animation and gaming by enabling independent adjustment of limb motions, representing an incremental improvement over whole-body motion encoding methods.
The paper tackles the problem of generating flexible and editable motion in-betweening for animation by modeling motion styles at the body-part level, resulting in enhanced diversity and controllability with superior speed and robust generalization in evaluations.
Styled motion in-betweening is crucial for computer animation and gaming. However, existing methods typically encode motion styles by modeling whole-body motions, often overlooking the representation of individual body parts. This limitation reduces the flexibility of infilled motion, particularly in adjusting the motion styles of specific limbs independently. To overcome this challenge, we propose a novel framework that models motion styles at the body-part level, enhancing both the diversity and controllability of infilled motions. Our approach enables more nuanced and expressive animations by allowing precise modifications to individual limb motions while maintaining overall motion coherence. Leveraging phase-related insights, our framework employs periodic autoencoders to automatically extract the phase of each body part, capturing distinctive local style features. Additionally, we effectively decouple the motion source from synthesis control by integrating motion manifold learning and conditional generation techniques from both image and motion domains. This allows the motion source to generate high-quality motions across various styles, with extracted motion and style features readily available for controlled synthesis in subsequent tasks. Comprehensive evaluations demonstrate that our method achieves superior speed, robust generalization, and effective generation of extended motion sequences.