RECAST: Reusing Human Priors for Data-Scarce Animal 3D Pose estimation

Monocular 3D animal pose estimation is inherently ambiguous, while the scarcity of 3D annotations makes reliable probabilistic 2D-to-3D lifting difficult to learn. We introduce RECAST, an efficient flow-matching framework that reuses human priors for data-scarce 3D animal pose estimation. RECAST employs a three-stage training strategy. First, probabilistic pretraining on abundant human poses learns a conditional 2D-to-3D flow that provides a transferable initialization for cross-species lifting. Second, low-rank feature and graph adaptation specializes the pretrained flow to animal morphology while retaining its conditional prediction structure. Third, skeletal refinement is introduced after adaptation has converged, further improving both pose accuracy and bone-length consistency. For efficient multi-hypothesis inference, we further propose Chordal Trajectory Branching (CTB). Inspired by the linear probability paths used in OT-CFM, CTB constructs intermediate hypothesis states through a closed-form approximation from a reference trajectory and integrates only their remaining trajectory suffixes. This avoids repeated prefix integration and substantially reduces redundant flow evaluations. RECAST achieves 29.86 mm and 18.67 mm P-MPJPE on Animal3D and CtrlAni3D, respectively. CTB matches standard multi-hypothesis inference within 0.01 mm while reducing function evaluations by 49%.