WildAni4D: Towards 4D Animal Mesh Reconstruction

Recovering 4D animal motion, including 3D geometry and global trajectory, is essential for quantitative biomechanics and behavioral analysis. However, two major challenges hinder the progress of animal motion recovery. Existing methods lack sufficient annotated video data and suffer from per-frame temporal instability. To address this, we introduce WildAni4D, a framework that unites a novel synthetic video generation pipeline with a new reconstruction model. First, our generator creates a large-scale dataset of realistic, appearance-consistent video sequences with groundtruth animal geometry and camera trajectories. Second, our reconstruction model robustly estimates temporally coherent motion using a single sequence-level shape and perframe pose predictions. We demonstrate that our model outperforms state-of-the-art per-frame methods, drastically reducing temporal pose flicker and shape drift. WildAni4D