ArtSplat: Feed-Forward Articulated Object Reconstruction via Gaussian Splatting

Articulated object reconstruction requires simultaneous inference of geometry and underlying articulation structure. Existing methods based on NeRF and 3D Gaussian Splatting (3DGS) typically rely on dense views or strong priors ($\text{e.g.}$, depth maps, joint types, predefined number of joints), requiring costly per-object optimization. In this paper, we propose ArtSplat, the first feed-forward 3DGS framework that reconstructs both geometry and joint parameters from sparse multi-view images across multiple articulation states in a single forward pass. Our novel per-pixel joint map representation enables integration of joint parameter estimation into the feed-forward pipeline. We further propose a Cross-State Attention (CSA) mechanism with state tokens, which effectively captures discrete motion across input states. Comprehensive experiments demonstrate competitive geometry reconstruction, accurate joint estimation and cross-dataset generalizability of ArtSplat on both single- and multi-joint objects. Compared to the per-object optimization baselines, ArtSplat achieves 389 times faster inference time.