UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing
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Cited by 22 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Recent advances in text-guided video editing have showcased promising results in appearance editing (e.g., stylization). However, video motion editing in the temporal dimension (e.g., from eating to waving), which distinguishes video editing from image editing, is underexplored. In this work, we present UniEdit, a tuning-free framework that supports both video motion and appearance editing by harnessing the power of a pre-trained text-to-video generator within an inversion-then-generation framework. To realize motion editing while preserving source video content, based on the insights that temporal and spatial self-attention layers encode inter-frame and intra-frame dependency respectively, we introduce auxiliary motion-reference and reconstruction branches to produce text-guided motion and source features respectively. The obtained features are then injected into the main editing path via temporal and spatial self-attention layers. Extensive experiments demonstrate that UniEdit covers video motion editing and various appearance editing scenarios, and surpasses the state-of-the-art methods. Our code will be publicly available.
Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 78 of its 91 references that are in the map. First posted on arXiv 20 Feb 2024.
Builds on
78 of 91 references.
References · 78
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- 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
- 2023ModelScope Text-to-Video Technical Reportcited by 651
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- 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 254
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50 most cited of 78 All 78 (JSON)
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Citing papers per year: 2024: 22; 2025: 14; 2026: 11.
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