Progressive Pose-Guided 4D Animal Reconstruction from Monocular Video

Reconstructing 4D animals from monocular videos is chal-lenging due to large inter-species variation, complex articulations, andthe lack of reliable templates. Existing approaches typically rely on eitherstrict category-specific priors that restrict generalization, or unconstrainedgenerative models that sacrifice input fidelity. To bridge this gap, wepresent a progressive test-time optimization framework built on 3D Gaus-sian Splatting for high-fidelity 4D animal reconstruction from a singlevideo. Our key insight is that a coarse shape prior suffices when coupledwith a progressive strategy that disentangles articulated pose from non-rigid deformation. Specifically, we employ a symmetry-aware temporalencoding that exploits bilateral cues while absorbing camera estimationdrift and a part-conditioned deformation mechanism guided by learn-able part anchors and a learnable skinning field. Extensive experimentsdemonstrate that our approach generalizes robustly across diverse species,achieving superior geometric accuracy, temporal consistency, and visualfidelity compared to existing baselines, even under severe prior mismatch.Project page: https://syl-322.github.io/ReWild4D/