# MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing

arXiv: https://arxiv.org/abs/2610.12963 · HTML: https://mlsanity.com/paper/2610.12963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Builds on (most cited first, 25 of 25)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [Seedance 2.0: Advancing Video Generation for World Complexity](https://mlsanity.com/paper/2604.14148/) (2026)
- [Automated Movie Generation via Multi-Agent CoT Planning](https://mlsanity.com/paper/2503.07314/) (2025)
- [Consistent Video-to-Video Transfer Using Synthetic Dataset](https://mlsanity.com/paper/2311.00213/) (2023)
- [Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset](https://mlsanity.com/paper/2510.15742/) (2025)
- [Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance](https://mlsanity.com/paper/2603.02175/) (2026)
- [JarvisEvo: Towards a Self-Evolving Photo Editing Agent with Synergistic Editor-Evaluator Optimization](https://mlsanity.com/paper/2511.23002/) (2025)
- [IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment](https://mlsanity.com/paper/2510.11647/) (2025)
- [GenAgent: Scaling Text-to-Image Generation via Agentic Multimodal Reasoning](https://mlsanity.com/paper/2601.18543/) (2026)
- [GEMS: Agent-Native Multimodal Generation with Memory and Skills](https://mlsanity.com/paper/2603.28088/) (2026)
- [Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling](https://mlsanity.com/paper/2602.09084/) (2026)
- [GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation](https://mlsanity.com/paper/2605.21605/) (2026)
- [VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment](https://mlsanity.com/paper/2408.11481/) (2024)
- [EditBoard: Towards a Comprehensive Evaluation Benchmark for Text-Based Video Editing Models](https://mlsanity.com/paper/2409.09668/) (2024)
- [TDVE-Assessor: Benchmarking and Evaluating the Quality of Text-Driven Video Editing with LMMs](https://mlsanity.com/paper/2505.19535/) (2025)
- [RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation](https://mlsanity.com/paper/2606.23221/) (2026)
- [M3: High-fidelity Text-to-Image Generation via Multi-Modal, Multi-Agent and Multi-Round Visual Reasoning](https://mlsanity.com/paper/2602.06166/) (2026)
- [VideoAgent: All-in-One Framework for Video Understanding and Editing](https://mlsanity.com/paper/2606.23327/) (2026)
- [PhotoAgent: Exploratory Visual Aesthetic Planning with Large Vision Models](https://mlsanity.com/paper/2602.22809/) (2026)
- [CoVEBench: Can Video Editing Models Handle Complex Instructions?](https://mlsanity.com/paper/2606.08415/) (2026)
- [WeAgent-MMGenEdit: A Full-Stack Recipe for Multimodal Agentic Image Generation and Editing](https://mlsanity.com/paper/2609.05171/) (2026)

## Abstract (arXiv)

Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Beyond general scenarios, professional motions are significant in performance, sports, and martial arts. This paper focuses on professional motion editing, which involves fine-grained motion technique and temporal structure. Given professional motion names but lacking detailed descriptions, existing models often fail to generate professional motions, such as incorrectly lifting the front wheel under a Stoppie instruction. 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. To supply training data, we build a data pipeline to construct trajectories. We cold-start the agent on MotionCoach-SFT-10k and apply Group Relative Policy Optimization (GRPO) on MotionCoach-RL-2k with rewards on the motion guidance prompt, the motion outcome video, and the format. For evaluation, we introduce VPMEBench, comprising 314 professional motion editing tasks with fine-grained references and annotations. Extensive experiments show that MotionCoach improves the overall score from 55.1 to 65.6 on Seedance 2.0 and from 51.2 to 64.5 on MiniMax-H3. Learning to construct multimodal guidance enables precise professional motion editing without retraining video editing models.