Preserve, Reveal, Expand: Faithful 4D Video Editing with Region-Aware Conditioning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913148087304192 |
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| author | Hu, Zhangchi Sun, Wenzhang Yin, Xiangchen Yuan, Jiahui Wang, Chunfeng Li, Hao Zhan, Kun Sun, Xiaoyan |
| author_facet | Hu, Zhangchi Sun, Wenzhang Yin, Xiangchen Yuan, Jiahui Wang, Chunfeng Li, Hao Zhan, Kun Sun, Xiaoyan |
| contents | Existing 4D-driven video diffusion models primarily target plausible generation, but faithful 4D editing requires preserving source-observed regions while synthesizing disoccluded or out-of-view content. We identify Evidence-Role Mismatch: reliable source-backed evidence, unreliable rendered cues, and unsupported regions are entangled in a single conditioning signal, causing preservation drift, ghosting, and unstable extrapolation. We propose PREX (Preserve, Reveal, Expand), a region-aware framework that decomposes the target spatiotemporal volume into Preserve, Reveal, and Expand roles according to observation support and scene extent. PREX builds observation-backed appearance cues with calibrated confidence and injects them into a frozen video diffusion backbone through a region-aware adapter, trained with proxy tasks without requiring paired edited videos. We further introduce PREBench, a diagnostic benchmark with curated edits, region-role masks, and human-aligned metrics that complement global video-quality and 4D-control evaluations. Experiments show that PREX reduces region-structured failures while maintaining strong visual quality and 4D edit control capability. Project Page: https://ricepastem.github.io/PREX-Open |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20961 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Preserve, Reveal, Expand: Faithful 4D Video Editing with Region-Aware Conditioning Hu, Zhangchi Sun, Wenzhang Yin, Xiangchen Yuan, Jiahui Wang, Chunfeng Li, Hao Zhan, Kun Sun, Xiaoyan Computer Vision and Pattern Recognition Existing 4D-driven video diffusion models primarily target plausible generation, but faithful 4D editing requires preserving source-observed regions while synthesizing disoccluded or out-of-view content. We identify Evidence-Role Mismatch: reliable source-backed evidence, unreliable rendered cues, and unsupported regions are entangled in a single conditioning signal, causing preservation drift, ghosting, and unstable extrapolation. We propose PREX (Preserve, Reveal, Expand), a region-aware framework that decomposes the target spatiotemporal volume into Preserve, Reveal, and Expand roles according to observation support and scene extent. PREX builds observation-backed appearance cues with calibrated confidence and injects them into a frozen video diffusion backbone through a region-aware adapter, trained with proxy tasks without requiring paired edited videos. We further introduce PREBench, a diagnostic benchmark with curated edits, region-role masks, and human-aligned metrics that complement global video-quality and 4D-control evaluations. Experiments show that PREX reduces region-structured failures while maintaining strong visual quality and 4D edit control capability. Project Page: https://ricepastem.github.io/PREX-Open |
| title | Preserve, Reveal, Expand: Faithful 4D Video Editing with Region-Aware Conditioning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.20961 |