SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view
  Human Reconstruction
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 photo-realistic 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.
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Wenyue Chen
claimed_verified
2025-10-28T14:19:50.569Z
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wenyue Chen
xishushu
user
xishushu
6900cd53646208eac0d1f044
Peng Li
6900cd53646208eac0d1f045
Wangguandong Zheng
6900cd53646208eac0d1f046
Chengfeng Zhao
6900cd53646208eac0d1f047
Mengfei Li
6900cd53646208eac0d1f048
Yaolong Zhu
6900cd53646208eac0d1f049
Zhiyang Dou
claimed_verified
2026-03-18T08:47:50.820Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/645223fb01d7bd9555ea399a/fVR7XmGg6pMSRKx9sEdvT.png
Zhiyang Dou
frankzydou
user
frankzydou
6900cd53646208eac0d1f04a
Ronggang Wang
6900cd53646208eac0d1f04b
Yuan Liu
SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view
  Human Reconstruction
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 photo-realistic 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.
SyncHuman integrates 2D multiview and 3D native generative models with pixel-aligned synchronization and feature injection to achieve high-quality, photo-realistic 3D human reconstruction from single images.
SMPL estimation SMPL-conditioned image generative models 2D multiview generative model 3D native generative model pixel-aligned 2D-3D synchronization attention feature injection mechanism