MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing
To address this challenge, we introduce MotionCoach, a trained multimodal agent that uses multi-turn tools and performs self-verification, producing a grounded prompt and a reference clip as reliable and sufficient multimodal guidance to inject external professional motion knowledge into video editing models.
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Key points
- Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data.
- This paper focuses on professional motion editing, which involves fine-grained motion technique and temporal structure.
- For evaluation, we introduce VPMEBench, comprising 314 professional motion editing tasks with fine-grained references and annotations.
- Learning to construct multimodal guidance enables precise professional motion editing without retraining video editing models.
Sources (1)
- [1]MotionCoach: Constructing Multimodal Guidance for Video Professional Motion EditingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 04:08 AM
To address this challenge, we introduce MotionCoach, a trained multimodal agent that uses multi-turn tools and performs self-verification, producing a grounded prompt and a reference clip as reliable and sufficient multimodal guidance to inject external professional motion knowledge into video editing models.
Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data.
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