Reproduce “Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion” on OpenResearch
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About this paper
Linzhan Mou, Jun-Kun Chen, Yu-Xiong Wang
A framework for instruction-guided dynamic (4D) scene editing converts 4D scenes into pseudo-3D representations, applying 2D diffusion models to achieve high-quality, spatially and temporally consistent modifications. The approach outperforms a baseline method in generating edited scenes with greater detail and coherence across various prompts and datasets.
Key insights
- Decomposing the 4D editing problem by treating dynamic scenes as a collection of view-dependent videos simplifies the task of achieving holistic spatial-temporal consistency.
- Distilling knowledge from 2D diffusion models into 4D NeRFs requires explicitly designed mechanisms, such as anchor-aware attention and optical flow guidance, to overcome inherent inconsistencies.
- An iterative optimization pipeline, where the NeRF is progressively refined by learning from consistently generated edited datasets, is crucial for converging to high-quality and stable 4D scene edits.
Summary by alphaXiv.
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