Preserve, Reveal, Expand: Faithful 4D Video Editing with Region-Aware Conditioning
It addresses the problem of faithful 4D video editing for practitioners, where existing methods cause preservation drift and ghosting; the work is incremental, building on frozen video diffusion models with a region-aware adapter.
PREX introduces a region-aware framework for faithful 4D video editing that decomposes the target volume into Preserve, Reveal, and Expand roles, reducing region-structured failures while maintaining visual quality and 4D control.
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