title: HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction
    via Gaussian Restoration
summary: 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 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, 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.
paper_authors._id: 67f364118188d683931bec4a
paper_authors.name: Boyuan Wang
paper_authors._id: 67f364118188d683931bec4b
paper_authors.name: Runqi Ouyang
paper_authors._id: 67f364118188d683931bec4c
paper_authors.name: Xiaofeng Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T14:24:59.872Z
paper_authors._id: 6426616ea5ec4a5cbc535634
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6426616ea5ec4a5cbc535634/hH6JsxnXeakH3mBTeNNmO.jpeg
paper_authors.fullname: JeffWang
paper_authors.type: user
paper_authors.user: Jeff-Wang
paper_authors._id: 67f364118188d683931bec4d
paper_authors.name: Zheng Zhu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-07T05:35:15.515Z
paper_authors._id: 656e9b562cd7a3e348011d26
paper_authors.avatarUrl: /avatars/bcca51bdc27c664f8f132420e6ed99fa.svg
paper_authors.fullname: Zheng Zhu
paper_authors.type: user
paper_authors.user: ZhengZhu
paper_authors._id: 67f364118188d683931bec4e
paper_authors.name: Guosheng Zhao
paper_authors._id: 67f364118188d683931bec4f
paper_authors.name: Chaojun Ni
paper_authors._id: 67f364118188d683931bec50
paper_authors.name: Guan Huang
paper_authors._id: 67f364118188d683931bec51
paper_authors.name: Lihong Liu
paper_authors._id: 67f364118188d683931bec52
paper_authors.name: Xingang Wang
paper_title: HumanDreamer-X: Photorealistic Single-image Human Avatars Reconstruction
    via Gaussian Restoration
paper_summary: 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 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, 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.
paper_ai_summary: A novel framework, HumanDreamer-X, enhances single-image human reconstruction through unified 3D Gaussian Splatting and HumanFixer, improving PSNR and handling in-the-wild data.
paper_ai_keywords: HumanDreamer-X HumanFixer 3D Gaussian Splatting multi-view human generation attention modulation photorealistic results PSNR quality metrics