Topology-Agnostic Animal Motion Generation from Text Prompt
Keyi Chen, Mingze Sun, Zhenyu Liu, Zhangquan Chen, Ruqi Huang

TL;DR
This paper introduces OmniZoo, a large-scale animal motion dataset, and a novel topology-agnostic generative framework that produces text-driven, physically plausible animal motions across diverse skeletal structures.
Contribution
The work presents a new dataset and a generalized autoregressive model with a topology-aware embedding for flexible, text-driven animal motion generation across different skeleton topologies.
Findings
Successfully generates coherent animal motions from text prompts.
Enables cross-species motion style transfer.
Handles arbitrary skeletal topologies with high fidelity.
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…
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Taxonomy
TopicsHuman Motion and Animation · Generative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis
