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New framework reconstructs 3D avatars from single images using diffusion models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel framework for reconstructing high-fidelity 3D avatars from single images by utilizing cascaded Low-Rank Adaptations (LoRAs) within a pre-trained diffusion model. This approach addresses the challenges of limited PBR data and disentangling illumination from material properties. The method employs specialized LoRAs for texture completion and light homogenization, along with a cross-intrinsic attention mechanism to generate physically plausible PBR maps, trained on fewer than 100 3D scans. AI

IMPACT This research could enable more efficient and realistic creation of 3D digital assets from limited visual data.

RANK_REASON The cluster describes a new research paper detailing a novel technical framework for 3D avatar reconstruction.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework reconstructs 3D avatars from single images using diffusion models

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Hong Li, Minqi Meng, Yanjun Liang, Chongjie Ye, Houyuan Chen, Weiqing Xiao, Xianda Guo, Guojun Lei, Xuhui Liu, Chaojie Yang, Yanlun Peng, Hao Zhao, Baochang Zhang ·

    Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading

    arXiv:2606.28144v1 Announce Type: new Abstract: Reconstructing high-fidelity, relightable 3D avatars from a single in-the-wild image is a challenging ill-posed problem, primarily hindered by the scarcity of high-quality PBR data and the complexity of disentangling illumination fr…

  2. arXiv cs.CV TIER_1 English(EN) · Baochang Zhang ·

    Monocular Avatar Reconstruction via Cascaded Diffusion Priors and UV-Space Differentiable Shading

    Reconstructing high-fidelity, relightable 3D avatars from a single in-the-wild image is a challenging ill-posed problem, primarily hindered by the scarcity of high-quality PBR data and the complexity of disentangling illumination from intrinsic materials. In this paper, we presen…

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