Identity-Preserving Human Reconstruction from a Single Image via 3D Token Inference

We present the Identity-Preserving Large Human Recon-struction Model (IPRM), a feed-forward framework that reconstructsphotorealistic, clothed 3D humans from a single in-the-wild image whilepreserving 3D identity. Recent works predominantly infer 3D structurefrom 2D features, making it challenging to achieve 3D consistency andpreserve human identity in 3D space. To alleviate these challenges, IPRManchors the single-view 3D human reconstruction by constructing a human-based 3D feature space and explicitly preserves the human 3D identityfeatures during inference. Specifically, we introduce an efficient and ro-bust SMPL-based sparse voxel representation to transform 2D imageidentity features into 3D space, categorizing them into 3D visible iden-tity tokens and 3D invisible tokens to be inferred. Using these 3D tokens,an identity-aware 3D token inference module is proposed to propagateprojected 3D identity features from visible to invisible tokens, ensuringthat only unobserved regions are predicted while the observed identityremains intact. We further design an encoder–decoder architecture thatdecodes SMPL-based 3D features into either a 3D Gaussian Splatting ormesh representation, and augment it with a 3D ID Adapter for identitypreservation. Instead of conventional conditioning on image tokens (2DID Adapter), this adapter utilizes 3D identity tokens extracted from aparallel identity branch as guidance to inject token-wise identity infor-mation. Comprehensive experiments on existing benchmarks and in-the-wild data show IPRM surpasses state-of-the-art methods in reconstruc-tion performance, efficiency, 3D consistency and identity consistency.