1 as the DiT video generator.
ThinkV2V uses explicit MLLM reasoning to boost complex video editing
The framework couples a thinking multimodal language model with a diffusion video generator, outperforming larger baselines on reasoning-intensive instructions.
Chinese Tech
Donghao Zhou · Haoyang He · Fan Zhang · Hao Yang · Guisheng Liu · Xin Gao · +7 more
The Chinese University of Hong Kong · Zhejiang University · ByteDance · The Ohio State University
Research Digest··2 min read
The authors present ThinkV2V, a reasoning-driven video editing framework that activates explicit thinking in a multimodal large language model (MLLM) before visual generation.
Why this paper
From ByteDance and 3 others
In one line
ThinkV2V improves instruction-guided video editing by making MLLMs reason explicitly before generating edits.
What we could check
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