Boosting 3D object generation through PBR materials

Normal BoostingFig. 1.Overview.Given a single image, the existing image-to-3D generative models always synthesize 3D meshes with flawed geometry and RGB textures only.Our method not only boosts existing approaches with PBR materials, empowering relighting under various lighting conditions, but also boosts the object's normal maps, capturing more intricate details and better aligning with the given image.Notably, we fine-tune Stable Diffusion to estimate the albedo map from the single-view RGB image and lift it to multi-view albedo maps for a complete albedo UV.

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Publication Details

Published
2024-12-03
DOI
https://doi.org/10.1145/3680528.3687676
Citations
3
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
0.75
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article

Boosting 3D object generation through PBR materials

Haoran Wang, Y. Wang, Bo Dai, Li Ma et al.
3 citations
Augmented Reality Applications
0.75
article

Boosting 3D object generation through PBR materials

Haoran Wang, Y. Wang, Bo Dai, Li Ma, Xudong Xu
article en
3 citations

Abstract

Normal BoostingFig. 1.Overview.Given a single image, the existing image-to-3D generative models always synthesize 3D meshes with flawed geometry and RGB textures only.Our method not only boosts existing approaches with PBR materials, empowering relighting under various lighting conditions, but also boosts the object's normal maps, capturing more intricate details and better aligning with the given image.Notably, we fine-tune Stable Diffusion to estimate the albedo map from the single-view RGB image and lift it to multi-view albedo maps for a complete albedo UV.

Shanghai Jiao Tong University (CN), Fudan University (CN), Beijing Academy of Artificial Intelligence (CN), Shanghai Artificial Intelligence Laboratory (CN)
Affordable and clean energy
Openalex Percentile: Top 26%
Augmented Reality Applications
0.75
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