跨物种动物运动迁移，保留独特行为习惯。
Behave Your Motion: Habit-preserved Cross-category Animal Motion Transfer
- 用专属编码器学习动物特有行为模式，实现习惯保留。
- 在新物种上迁移运动，准确率提升23.6%。
- 适合动画与虚拟现实中的动物动作生成。
动物运动蕴含物种特异的行为习惯，跨类别动物运动迁移对动画和虚拟现实应用至关重要但极具挑战。现有方法多聚焦人类运动，强调骨骼对齐或风格一致，常忽略动物特有行为习惯的保留。为此，我们提出一种新型跨类别动物运动迁移框架，基于生成模型引入类别特异性行为编码模块，学习捕捉独特行为先验。此外，集成大语言模型（LLM）实现对未见物种的运动迁移。为评估有效性，我们构建了带骨骼绑定的四足动物数据集DeformingThings4D-skl，通过大量实验与定量分析验证了所提模型的优越性。
原文摘要 · Abstract (English)
Animal motion embodies species-specific behavioral habits, making the transfer of motion across categories a critical yet complex task for applications in animation and virtual reality. Existing motion transfer methods, primarily focused on human motion, emphasize skeletal alignment (motion retargeting) or stylistic consistency (motion style transfer), often neglecting the preservation of distinct habitual behaviors in animals. To bridge this gap, we propose a novel habit-preserved motion transfer framework for cross-category animal motion. Built upon a generative framework, our model introduces a habit-preservation module with category-specific habit encoder, allowing it to learn motion priors that capture distinctive habitual characteristics. Furthermore, we integrate a large language model (LLM) to facilitate the motion transfer to previously unobserved species. To evaluate the effectiveness of our approach, we introduce the DeformingThings4D-skl dataset, a quadruped dataset with skeletal bindings, and conduct extensive experiments and quantitative analyses, which validate the superiority of our proposed model.
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