FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912729959235584 |
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| author | Wang, YuAn Li, Xiaofan Huang, Chi Zhang, Wenhao Li, Hao Wang, Bosheng Sun, Xun Wang, Jun |
| author_facet | Wang, YuAn Li, Xiaofan Huang, Chi Zhang, Wenhao Li, Hao Wang, Bosheng Sun, Xun Wang, Jun |
| contents | In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce \textbf{FaithFusion}, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural modifications.Extensive experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift. Our code is available at https://github.com/wangyuanbiubiubiu/FaithFusion. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_21113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain Wang, YuAn Li, Xiaofan Huang, Chi Zhang, Wenhao Li, Hao Wang, Bosheng Sun, Xun Wang, Jun Computer Vision and Pattern Recognition In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce \textbf{FaithFusion}, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural modifications.Extensive experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift. Our code is available at https://github.com/wangyuanbiubiubiu/FaithFusion. |
| title | FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.21113 |