SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction

NeurIPS 2025
Wenyue Chen1, Peng Li2†, Wangguandong Zheng3, Chengfeng Zhao2, Mengfei Li2, Yaolong Zhu1, Zhiyang Dou4, Ronggang Wang1, Yuan Liu2†
1Peking University, 2The Hong Kong University of Science and Technology, 3Southeast University,
4The University of Hong Kong
Paper Code
SyncHuman Demo

We introduce SyncHuman, a full-body human reconstruction model using synchronized
2D and 3D generative model. Given a single image of a clothed person, our method
generates detailed geometry and lifelike 3D human appearances across diverse poses.

Abstract

Photorealistic 3D full-body human reconstruction from a single image is a critical yet challenging task for applications in films and video games due to inherent ambiguities and severe self-occlusions. While recent approaches leverage SMPL estimation and SMPL-conditioned image generative models to hallucinate novel views, they suffer from inaccurate 3D priors estimated from SMPL meshes and have difficulty in handling difficult human poses and reconstructing fine details.In this paper, we propose SyncHuman, a novel framework that combines 2D multiview generative model and 3D native generative model for the first time, enabling high-quality clothed human mesh reconstruction from single-view images even under challenging human poses.Multiview generative model excels at capturing fine 2D details but struggles with structural consistency, whereas 3D native generative model generates coarse yet structurally consistent 3D shapes. By integrating the complementary strengths of these two approaches, we develop a more effective generation framework. Specifically, we first jointly fine-tune the multiview generative model and the 3D native generative model with proposed pixel-aligned 2D-3D synchronization attention to produce geometrically aligned 3D shapes and 2D multiview images. To further improve details, we introduce a feature injection mechanism that lifts fine details from 2D multiview images onto the aligned 3D shapes, enabling accurate and high-fidelity reconstruction.Extensive experiments demonstrate that SyncHuman achieves robust and photorealistic 3D human reconstruction, even for images with challenging poses. Our method outperforms baseline methods in geometric accuracy and visual fidelity, demonstrating a promising direction for future 3D generation models.

Method

SyncHuman Demo

Given a single human image, SyncHuman first generates multiview color and normal maps, along with an aligned sparse voxel grid, which is further transformed into a set of structured latents. Then, we propose to inject the high-quality images into the 3D latents via a Multiview Guided Decoder and output the detailed high-fidelity textured human mesh.

SyncHuman Demo

2D-3D synchronization attention. 2D to 3D attention: each 3D voxel feature is orthogonally projected onto front, back, left, and right view planes to retrieve corresponding 2D features, and refines the voxel feature with cross-attention. 3D to 2D attention: each 2D multiview feature is projected into 3D space to attend to a column of voxel features, enhancing the 2D features.This mutual refinement ensures that 2D generative model and 3D generative model align with each other in a shared 3D space.Given a single human image, SyncHuman first generates multiview color and normal maps, along with an aligned sparse voxel grid, which is further transformed into a set of structured latents. Then, we propose to inject the high-quality images into the 3D latents via a Multiview Guided Decoder and output the detailed high-fidelity textured human mesh.

Comparision

More Results

BibTeX

@article{chen2025synchuman,
        title={SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view Human Reconstruction}, 
         author={Wenyue Chen, Peng Li, Wangguandong Zheng, Chengfeng Zhao, Mengfei Li, Yaolong Zhu, Zhiyang Dou, Ronggang Wang, Yuan Liu},
         year={2025},
         journal={arXiv preprint arXiv:2510.07723}
        }