TIGER-AI-Lab/AnyV2V
A tuning-free video editing framework published at TMLR 2024 that converts video editing to an image editing problem using diffusion models.
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AnyV2V is a training-free framework for performing diverse video-to-video editing tasks by leveraging image editing techniques. The method converts video editing into an image editing problem, requiring only a single reference image to achieve temporally consistent edits. It builds on top of existing image editing methods to enable various editing operations including style transfer, object replacement, and attribute changes across video sequences.
Frequently asked
- What is TIGER-AI-Lab/AnyV2V?
- A tuning-free video editing framework published at TMLR 2024 that converts video editing to an image editing problem using diffusion models.
- Is AnyV2V open source?
- Yes — TIGER-AI-Lab/AnyV2V is open source, released under the MIT license.
- What language is AnyV2V written in?
- TIGER-AI-Lab/AnyV2V is primarily written in Jupyter Notebook.
- How popular is AnyV2V?
- TIGER-AI-Lab/AnyV2V has 654 stars on GitHub.
- Where can I find AnyV2V?
- TIGER-AI-Lab/AnyV2V is on GitHub at https://github.com/TIGER-AI-Lab/AnyV2V.