Donghao Zhou

The work proposes ThinkV2V, a reasoning-driven video-editing framework that explicitly activates multimodal large language model thinking before visual generation: it uses Qwen3-VL-Thinking-8B to perform chain-of-thought reasoning over the source video and instruction and output a refined prompt, injects the hidden states into a Wan-2.1-5B DiT through a learnable-query connector, pairs this with Progressive Curriculum Training and Inference-Time Thinking Scaling, and builds the ThinkV2V-150K dataset and ThinkV2V-Bench; under two judge models, the 5B-scale model achieves the best overall scores on both complex and standard editing scenarios and surpasses several 10B-scale baselines.

The work proposes ThinkV2V, a reasoning-driven video-editing framework that explicitly activates multimodal large language model thinking before visual generation: it uses Qwen3-VL-Thinking-8B to perform chain-of-thought reasoning over the source video and instruction and output a refined prompt, injects the hidden states into a Wan-2.1-5B DiT through a learnable-query connector, pairs this with Progressive Curriculum Training and Inference-Time Thinking Scaling, and builds the ThinkV2V-150K dataset and ThinkV2V-Bench; under two judge models, the 5B-scale model achieves the best overall scores on both complex and standard editing scenarios and surpasses several 10B-scale baselines.