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Computer Science > Computer Vision and Pattern Recognition

arXiv:2310.16858 (cs)
[Submitted on 25 Oct 2023 (v1), last revised 6 Nov 2023 (this version, v2)]

Title:4D-Editor: Interactive Object-level Editing in Dynamic Neural Radiance Fields via Semantic Distillation

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Abstract:This paper targets interactive object-level editing (e.g., deletion, recoloring, transformation, composition) in dynamic scenes. Recently, some methods aiming for flexible editing static scenes represented by neural radiance field (NeRF) have shown impressive synthesis quality, while similar capabilities in time-variant dynamic scenes remain limited. To solve this problem, we propose 4D-Editor, an interactive semantic-driven editing framework, allowing editing multiple objects in a dynamic NeRF with user strokes on a single frame. We propose an extension to the original dynamic NeRF by incorporating a hybrid semantic feature distillation to maintain spatial-temporal consistency after editing. In addition, we design Recursive Selection Refinement that significantly boosts object segmentation accuracy within a dynamic NeRF to aid the editing process. Moreover, we develop Multi-view Reprojection Inpainting to fill holes caused by incomplete scene capture after editing. Extensive experiments and editing examples on real-world demonstrate that 4D-Editor achieves photo-realistic editing on dynamic NeRFs. Project page:this https URL
Comments:Project page:this https URL
Subjects:Computer Vision and Pattern Recognition (cs.CV)
Cite as:arXiv:2310.16858 [cs.CV]
 (orarXiv:2310.16858v2 [cs.CV] for this version)
 https://doi.org/10.48550/arXiv.2310.16858
arXiv-issued DOI via DataCite

Submission history

From: Dadong Jiang [view email]
[v1] Wed, 25 Oct 2023 02:20:03 UTC (19,184 KB)
[v2] Mon, 6 Nov 2023 03:38:44 UTC (20,599 KB)
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