FACt: Feed-forward Animatable Clothed Human Reconstruction

We present FACt, a novel feed-forward framework for animatable clothed human reconstruction from sparse-view images. Unlike optimization-based methods requiring per-subject refinement or feed-forward approaches limited to static capture, FACt leverages 3D Gaussian Splatting to enable high-fidelity avatar reconstruction and adaptive clothing deformation in a single forward pass. Our model's architecture efficiently fuses 3D representation with locally aligned image features via a deformable transformer, effectively preserving geometric and appearance fidelity in the canonical space. To accurately model dynamic clothing, we introduce an adaptive cloth joint learning strategy. This strategy extends the standard Linear Blend Skinning kinematic chain beyond the SMPL-X template, enabling realistic motion control across diverse clothing styles and poses. Experiments on real-world datasets demonstrate that FACt produces realistic and animatable human avatars across novel views and poses, outperforming state-of-the-art methods.