# MotionFlow: Attention-Driven Motion Transfer in Video Diffusion Models

arXiv: https://arxiv.org/abs/2412.05275 · HTML: https://mlsanity.com/paper/2412.05275/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 30)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [MagicVideo: Efficient Video Generation With Latent Diffusion Models](https://mlsanity.com/paper/2211.11018/) (2022)
- [ControlVideo: Training-free Controllable Text-to-Video Generation](https://mlsanity.com/paper/2305.13077/) (2023)
- [DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory](https://mlsanity.com/paper/2308.08089/) (2023)
- [What the DAAM: Interpreting Stable Diffusion Using Cross Attention](https://mlsanity.com/paper/2210.04885/) (2022)
- [VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning](https://mlsanity.com/paper/2309.15091/) (2023)
- [Boximator: Generating Rich and Controllable Motions for Video Synthesis](https://mlsanity.com/paper/2402.01566/) (2024)
- [Motion-Conditioned Diffusion Model for Controllable Video Synthesis](https://mlsanity.com/paper/2304.14404/) (2023)
- [TrailBlazer: Trajectory Control for Diffusion-Based Video Generation](https://mlsanity.com/paper/2401.00896/) (2023)
- [LLM-grounded Video Diffusion Models](https://mlsanity.com/paper/2309.17444/) (2023)
- [Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation](https://mlsanity.com/paper/2403.16990/) (2024)
- [Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models](https://mlsanity.com/paper/2402.14780/) (2024)
- [CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models](https://mlsanity.com/paper/2312.06059/) (2023)
- [Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning](https://mlsanity.com/paper/2305.13840/) (2023)
- [UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing](https://mlsanity.com/paper/2402.13185/) (2024)
- [Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition](https://mlsanity.com/paper/2403.14148/) (2024)
- [Motion Inversion for Video Customization](https://mlsanity.com/paper/2403.20193/) (2024)
- [MotionCrafter: One-Shot Motion Customization of Diffusion Models](https://mlsanity.com/paper/2312.05288/) (2023)
- [Animate Your Motion: Turning Still Images into Dynamic Videos](https://mlsanity.com/paper/2403.10179/) (2024)
- [Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation](https://mlsanity.com/paper/2403.19776/) (2024)

## Built on by (most cited first, 17 of 17)

- [LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers](https://mlsanity.com/paper/2505.23758/) (2025)
- [Dynamic View Synthesis as an Inverse Problem](https://mlsanity.com/paper/2506.08004/) (2025)
- [DualCamCtrl: Dual-Branch Diffusion Model for Geometry-Aware Camera-Controlled Video Generation](https://mlsanity.com/paper/2511.23127/) (2025)
- [SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation](https://mlsanity.com/paper/2506.23690/) (2025)
- [Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation](https://mlsanity.com/paper/2604.10030/) (2026)
- [Versatile Editing of Video Content, Actions, and Dynamics without Training](https://mlsanity.com/paper/2603.17989/) (2026)
- [LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer](https://mlsanity.com/paper/2505.14167/) (2025)
- [ElasticTTT: Prior-Preserving Test-Time Tuning for Video Editing](https://mlsanity.com/paper/2607.21529/) (2026)
- [Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling](https://mlsanity.com/paper/2504.05537/) (2025)
- [IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation](https://mlsanity.com/paper/2602.07498/) (2026)
- [STR-Match: Matching SpatioTemporal Relevance Score for Training-Free Video Editing](https://mlsanity.com/paper/2506.22868/) (2025)
- [VRWKV-Editor: Reducing quadratic complexity in transformer-based video editing](https://mlsanity.com/paper/2509.25998/) (2025)
- [Motion Marionette: Rethinking Rigid Motion Transfer via Prior Guidance](https://mlsanity.com/paper/2511.19909/) (2025)
- [CoMoGen: COntrollable MOtion Dynamics and Interactions with Mask-Guided Video GENeration](https://mlsanity.com/paper/2605.22996/) (2026)
- [DTG-Restore: Training-Free Diffusion Refinement for Generative Video Super-Resolution](https://mlsanity.com/paper/2605.30431/) (2026)
- [ProxyUp: Training-Free Proxy-Conditioned Video Generation for Controllable Dynamics](https://mlsanity.com/paper/2607.03732/) (2026)
- [Diverse Motion Customization via Control-based Dynamic Optimization](https://mlsanity.com/paper/2610.07911/) (2026)

## Abstract (arXiv)

Text-to-video models have demonstrated impressive capabilities in producing diverse and captivating video content, showcasing a notable advancement in generative AI. However, these models generally lack fine-grained control over motion patterns, limiting their practical applicability. We introduce MotionFlow, a novel framework designed for motion transfer in video diffusion models. Our method utilizes cross-attention maps to accurately capture and manipulate spatial and temporal dynamics, enabling seamless motion transfers across various contexts. Our approach does not require training and works on test-time by leveraging the inherent capabilities of pre-trained video diffusion models. In contrast to traditional approaches, which struggle with comprehensive scene changes while maintaining consistent motion, MotionFlow successfully handles such complex transformations through its attention-based mechanism. Our qualitative and quantitative experiments demonstrate that MotionFlow significantly outperforms existing models in both fidelity and versatility even during drastic scene alterations.