← 研踪 PaperTrailCVF · CVPR 2026:cvf:454d6b7f57ade208bc687cedbabd6065 · 记录 #1
Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:48
摘要
Although recent 3D-native generators have made great progress in synthesizing reliable geometry, they still fall short in achieving realistic appearances. A key obstacle lies in the lack of diverse and high-quality real-world 3D assets with rich surface details, since capturing such data is intrinsically difficult due to the diverse scales of scenes, non-rigid motions of objects, and the limited precision of scanners.We introduce Photo3D, a framework for advancing photorealistic 3D generation, which is driven by the image data generated by the GPT-4o-Image model.Considering that the generated images can distort 3D structures due to their lack of multi-view consistency, we design a structure-aligned multi-view synthesis pipeline and construct a detail-enhanced multi-view dataset paired with 3D geometry. Building on it, we present a realistic detail enhancement scheme that leverages perceptual feature adaptation and semantic structure matching to enforce appearance consistency with the realistic detail priors while preserving the structural consistency with the 3D-native geometry. While our scheme is general to different 3D-native generators, we present dedicated training strategies to facilitate the optimization of geometry-texture coupled and decoupled 3D-native generation paradigms. Experiments demonstrate that Photo3D generalizes well across diverse 3D-native generation paradigms and achieves state-of-the-art photorealistic 3D generation performance. Project Page: https://liangsanzhu.github.io/photo3d-page/
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记录 #1 · 2026 · 最新观测版本
Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement
Although recent 3D-native generators have made great progress in synthesizing reliable geometry, they still fall short in achieving realistic appearances. A key obstacle lies in the lack of diverse and high-quality real-world 3D assets with rich surface details, since capturing such data is intrinsically difficult due to the diverse scales of scenes, non-rigid motions of objects, and the limited precision of scanners.We introduce Photo3D, a framework for advancing photorealistic 3D generation, which is driven by the image data generated by the GPT-4o-Image model.Considering that the generated images can distort 3D structures due to their lack of multi-view consistency, we design a structure-aligned multi-view synthesis pipeline and construct a detail-enhanced multi-view dataset paired with 3D geometry. Building on it, we present a realistic detail enhancement scheme that leverages perceptual feature adaptation and semantic structure matching to enforce appearance consistency with the realistic detail priors while preserving the structural consistency with the 3D-native geometry. While our scheme is general to different 3D-native generators, we present dedicated training strategies to facilitate the optimization of geometry-texture coupled and decoupled 3D-native generation paradigms. Experiments demonstrate that Photo3D generalizes well across diverse 3D-native generation paradigms and achieves state-of-the-art photorealistic 3D generation performance. Project Page: https://liangsanzhu.github.io/photo3d-page/
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