Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement

• URL: http://arxiv.org/abs/2512.08535v1
• Date: Tue, 09 Dec 2025 12:33:48 GMT
• Title: Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement
• Authors: Xinyue Liang, Zhinyuan Ma, Lingchen Sun, Yanjun Guo, Lei Zhang,
• Abstract summary: Photo3D is a framework for advancing 3D generation driven by the GPT-4o-Image model image data.<n>We present a realistic detail enhancement scheme that leverages perceptual feature adaptation and semantic structure matching to enforce appearance consistency.<n>Our scheme is general to different 3D-native generators, and we present dedicated training strategies to facilitate the optimization of geometry-texture coupled and decoupled 3D-native generation paradigms.
• Score: 12.855027334688382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 texture 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 3D 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 realistic details while preserving the structural consistency with the 3D-native geometry. Our scheme is general to different 3D-native generators, and 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.

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