Abstract
Motion generation is fundamental to computer animation and widely used across entertainment, robotics, and virtual environments. While recent methods achieve impressive results, most rely on fixed skeletal templates, which prevent them from generalizing to skeletons with different or perturbed topologies. We address the core limitation of current motion generation methods - the combined lack of large-scale heterogeneous animal motion data and unified generative frameworks capable of jointly modeling arbitrary skeletal topologies and textual conditions. To this end, we introduce OmniZoo, a large-scale animal motion dataset spanning 140 species and 32,979 sequences, enriched with multimodal annotations. Building on OmniZoo, we propose a generalized autoregressive motion generation framework capable of producing text-driven motions for arbitrary skeletal topologies. Central to our model is a Topology-aware Skeleton Embedding Module that encodes geometric and structural properties of any skeleton into a shared token space, enabling seamless fusion with textual semantics. Given a text prompt and a target skeleton, our method generates temporally coherent, physically plausible, and semantically aligned motions, and further enables cross-species motion style transfer.
Abstract (translated)
运动生成是计算机动画的基础，并广泛应用于娱乐、机器人技术和虚拟环境等领域。尽管最近的方法取得了令人印象深刻的结果，但大多数方法依赖于固定的骨骼模板，这限制了它们对不同或受到干扰的拓扑结构的适应能力。我们解决了当前运动生成方法的核心局限性——缺乏大规模异构动物运动数据和统一的生成框架，这些框架能够同时建模任意骨骼拓扑和文本条件。为此，我们引入了OmniZoo，这是一个包含140种物种、共32,979个序列的大规模动物运动数据集，并且包含了多模式注释。基于OmniZoo，我们提出了一种通用的自回归运动生成框架，能够为任意骨骼拓扑生成文本驱动的动作。我们的模型核心是一个感知拓扑结构的骨架嵌入模块，该模块将任何骨骼的几何和结构性质编码到共享令牌空间中，从而能够无缝融合文本语义。在给定一个文本提示和目标骨骼后，我们的方法可以生成时间上连贯、物理上合理且语义对齐的动作，并进一步支持跨物种动作风格转换。
URL
https://arxiv.org/abs/2512.10352
