MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent
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Abstract (arXiv)
We propose MotionAgent, enabling fine-grained motion control for text-guided image-to-video generation. The key technique is the motion field agent that converts motion information in text prompts into explicit motion fields, providing flexible and precise motion guidance. Specifically, the agent extracts the object movement and camera motion described in the text and converts them into object trajectories and camera extrinsics, respectively. An analytical optical flow composition module integrates these motion representations in 3D space and projects them into a unified optical flow. An optical flow adapter takes the flow to control the base image-to-video diffusion model for generating fine-grained controlled videos. The significant improvement in the Video-Text Camera Motion metrics on VBench indicates that our method achieves precise control over camera motion. We construct a subset of VBench to evaluate the alignment of motion information in the text and the generated video, outperforming other advanced models on motion generation accuracy.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 71 of its 83 references that are in the map. First posted on arXiv 5 Feb 2025.
Builds on
71 of 83 references.
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50 most cited of 71 All 71 (JSON)
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Citing papers per year: 2025: 5; 2026: 6.
11 papers.
- 2025Controllable Video Generation: A Surveycited by 93
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- 2025VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generationcited by 25
- 2025MAViS: A Multi-Agent Framework for Long-Sequence Video Storytellingcited by 19
- 2025Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initializationcited by 9
- 2026CoMoVi: Co-Generation of 3D Human Motions and Realistic Videoscited by 4
- 2025SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognitioncited by 1
- 2026AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecastingcited by 0
- 2026FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-Oncited by 0
- 2026Agentic Visual Generation: From Generative Models to Agentic Controlcited by 0
- 2026CompAdapt: Adaptable Composite Motion Modeling for Physics-Consistent Text-to-Video Generationcited by 0
- 2026CompAdapt: Adaptable Composite Motion Modeling for Physics-Consistent Text-to-Video Generation18 Sep 2026
- 2026Agentic Visual Generation: From Generative Models to Agentic Control6 Sep 2026
- 2026FlowVVTON: Flow-Guided Mask-Free Video Virtual Try-On31 Aug 2026
- 2026AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting16 Mar 2026
- 2026CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos15 Jan 2026
- 2026VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control8 Jan 2026
- 2025SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition27 Nov 2025
- 2025MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling11 Aug 2025
- 2025Controllable Video Generation: A Survey22 Jul 2025
- 2025Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization11 Apr 2025
- 2025VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation11 Feb 2025
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