HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction via Gaussian Restoration
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908646212894720 |
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| author | Wang, Boyuan Ouyang, Runqi Wang, Xiaofeng Zhu, Zheng Zhao, Guosheng Ni, Chaojun Zhang, Xiaopei Huang, Guan Ren, Yijie Liu, Lihong Wang, Xingang |
| author_facet | Wang, Boyuan Ouyang, Runqi Wang, Xiaofeng Zhu, Zheng Zhao, Guosheng Ni, Chaojun Zhang, Xiaopei Huang, Guan Ren, Yijie Liu, Lihong Wang, Xingang |
| contents | Single-image human reconstruction is vital for digital human modeling applications but remains an extremely challenging task. Current approaches rely on generative models to synthesize multi-view images for subsequent 3D reconstruction and animation. However, directly generating multiple views from a single human image suffers from geometric inconsistencies, resulting in issues like fragmented or blurred limbs in the reconstructed models. To tackle these limitations, we introduce \textbf{HumanDreamer-X}, a novel framework that integrates multi-view human generation and reconstruction into a unified pipeline, which significantly enhances the geometric consistency and visual fidelity of the reconstructed 3D models. In this framework, 3D Gaussian Splatting serves as an explicit 3D representation to provide initial geometry and appearance priority. Building upon this foundation, \textbf{HumanFixer} is trained to restore 3DGS renderings, which guarantee photorealistic results. Furthermore, we delve into the inherent challenges associated with attention mechanisms in multi-view human generation, and propose an attention modulation strategy that effectively enhances geometric details identity consistency across multi-view. Experimental results demonstrate that our approach markedly improves generation and reconstruction PSNR quality metrics by 16.45% and 12.65%, respectively, achieving a PSNR of up to 25.62 dB, while also showing generalization capabilities on in-the-wild data and applicability to various human reconstruction backbone models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction via Gaussian Restoration Wang, Boyuan Ouyang, Runqi Wang, Xiaofeng Zhu, Zheng Zhao, Guosheng Ni, Chaojun Zhang, Xiaopei Huang, Guan Ren, Yijie Liu, Lihong Wang, Xingang Computer Vision and Pattern Recognition Single-image human reconstruction is vital for digital human modeling applications but remains an extremely challenging task. Current approaches rely on generative models to synthesize multi-view images for subsequent 3D reconstruction and animation. However, directly generating multiple views from a single human image suffers from geometric inconsistencies, resulting in issues like fragmented or blurred limbs in the reconstructed models. To tackle these limitations, we introduce \textbf{HumanDreamer-X}, a novel framework that integrates multi-view human generation and reconstruction into a unified pipeline, which significantly enhances the geometric consistency and visual fidelity of the reconstructed 3D models. In this framework, 3D Gaussian Splatting serves as an explicit 3D representation to provide initial geometry and appearance priority. Building upon this foundation, \textbf{HumanFixer} is trained to restore 3DGS renderings, which guarantee photorealistic results. Furthermore, we delve into the inherent challenges associated with attention mechanisms in multi-view human generation, and propose an attention modulation strategy that effectively enhances geometric details identity consistency across multi-view. Experimental results demonstrate that our approach markedly improves generation and reconstruction PSNR quality metrics by 16.45% and 12.65%, respectively, achieving a PSNR of up to 25.62 dB, while also showing generalization capabilities on in-the-wild data and applicability to various human reconstruction backbone models. |
| title | HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction via Gaussian Restoration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.03536 |