Abstract: Text-driven motion generation has made significant strides in recent years. However, most work has primarily focused on human motion, largely ignoring the rich and diverse behaviors exhibited by animals. Modeling animal motion has important applications in wildlife conservation, animal ecology, and biomechanics. Animal motion modeling presents unique challenges due to species diversity, varied morphological structures, and different behavioral patterns in response to identical textual descriptions.To address these challenges, we propose \textbf{AniMo} for text-driven animal motion generation. AniMo consists of two stages: motion tokenization and text-to-motion generation. In the motion tokenization stage, we encode motions using a joint-aware spatiotemporal encoder with species-aware feature adaptive modulation, enabling the model to adapt to diverse skeletal structures across species. In the text-to-motion generation stage, we employ masked modeling to jointly learn the mappings between textual descriptions and motion tokens.Additionally, we introduce AniMo4D, a large-scale dataset containing $78,149$ motion sequences and $185,435$ textual descriptions across $114$ animal species.Experimental results show that AniMo achieves superior performance on both the AniMo4D and AnimalML3D datasets, effectively capturing diverse morphological structures and behavioral patterns across various animal species.