PRIMA: Boosting Animal Mesh Recovery with Taxonomic Priors and Adaptation-Driven Data Bootstrapping
Xiaohang Yu, Ti Wang, Mackenzie Weygandt Mathis
Recovering articulated 3D animal meshes from monocular images is challenging due to the long-tailed distribution of species and limited large-scale 3D supervision. Existing methods rely on small annotated datasets and thus regress to average shapes and poses, leading to poor generalization to rare species and articulations. We present PRIMA (PRIors for Mesh Adaptation), a framework that tackles these limitations with two components: taxonomic shape priors and adaptation-driven data bootstrapping. First, we derive taxonomic shape priors from BioCLIP image features, which are learned under taxonomic supervision, to inject species-level knowledge into mesh reconstruction, improving shape estimation across diverse quadrupeds. Second, we propose an adaptation-driven bootstrapping scheme that turns abundant 2D animal annotations into reliable pseudo-3D supervision via lightweight instance-level mesh refinement and quality-aware filtering. This yields Quadruped3D, a large-scale pseudo-3D dataset constructed from real-world animal images, containing approximately 9K pseudo-3D annotations. Together, these components alleviate the long-tail problem: extensive experiments on Animal3D, CtrlAni3D, Quadruped2D, and Animal Kingdom demonstrate state-of-the-art performance for mesh reconstruction, with particularly strong gains on challenging poses and underrepresented species. The code is available at https://github.com/AdaptiveMotorControlLab/PRIMA.
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