What is it about?
Creating a long AI video is difficult: movements can become inconsistent, characters can change between scenes, and generation can require substantial graphics memory. We explore a different starting point: existing videos. A sequence of written scene descriptions is used to find matching moments in a video collection. AI editing then changes the subject or setting while using the source footage to guide motion. The framework also explores personalized subjects and transitions between clips. In tests with about 100 edited videos, the version using the VidToMe editor improved several quality measures over the two tested generation baselines. The paper reports graphics-memory use of 8-10 GB for its Stable Diffusion-based setup.
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Why is it important?
Real footage provides useful guidance about how people and objects move. Connecting video retrieval with AI editing offers a practical way to reuse that guidance when creating a new sequence of scenes. Editing clips or frames separately also makes longer-video creation possible within the memory budget reported in the study. The work opens a direction for more controllable video creation, while the quality still depends on the available footage, the editor and the transitions between clips.
Perspectives
For me, the interesting idea is to connect finding a video moment with creating a new one. Retrieved footage can provide a foundation for motion, while editing gives room to change the subject or setting. I see this work as a starting point for bringing retrieval, editing and scene transitions together in longer video creation.
Wei Feng
Tsinghua University
Read the Original
This page is a summary of: Multi-sentence Video Grounding for Long Video Generation, June 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icme59968.2025.11209951.
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