# Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer

arXiv: https://arxiv.org/abs/2406.06508 · HTML: https://mlsanity.com/paper/2406.06508/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 54 of its 79 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Placed in

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

## Builds on (most cited first, 30 of 54)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [MotionGPT: Human Motion as a Foreign Language](https://mlsanity.com/paper/2306.14795/) (2023)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [TokenFlow: Consistent Diffusion Features for Consistent Video Editing](https://mlsanity.com/paper/2307.10373/) (2023)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [An Edit Friendly DDPM Noise Space: Inversion and Manipulations](https://mlsanity.com/paper/2304.06140/) (2023)
- [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://mlsanity.com/paper/2310.08580/) (2023)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)

## Built on by (most cited first, 30 of 31)

- [CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control](https://mlsanity.com/paper/2410.03441/) (2024)
- [SMooDi: Stylized Motion Diffusion Model](https://mlsanity.com/paper/2407.12783/) (2024)
- [SimMotionEdit: Text-Based Human Motion Editing with Motion Similarity Prediction](https://mlsanity.com/paper/2503.18211/) (2025)
- [Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing](https://mlsanity.com/paper/2410.18977/) (2024)
- [HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization](https://mlsanity.com/paper/2506.15625/) (2025)
- [Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion](https://mlsanity.com/paper/2503.19557/) (2025)
- [PersonaBooth: Personalized Text-to-Motion Generation](https://mlsanity.com/paper/2503.07390/) (2025)
- [Attention (as Discrete-Time Markov) Chains](https://mlsanity.com/paper/2507.17657/) (2025)
- [Motion-example-controlled Co-speech Gesture Generation Leveraging Large Language Models](https://mlsanity.com/paper/2507.20220/) (2025)
- [StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion](https://mlsanity.com/paper/2503.21775/) (2025)
- [MotionVerse: A Unified Multimodal Framework for Motion Comprehension, Generation and Editing](https://mlsanity.com/paper/2509.23635/) (2025)
- [Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning](https://mlsanity.com/paper/2605.30969/) (2026)
- [Real-time Diverse Motion In-betweening with Space-time Control](https://mlsanity.com/paper/2410.00270/) (2024)
- [Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark](https://mlsanity.com/paper/2508.12438/) (2025)
- [SimGenHOI: Physically Realistic Whole-Body Humanoid-Object Interaction via Generative Modeling and Reinforcement Learning](https://mlsanity.com/paper/2508.14120/) (2025)
- [VQ-Style: Disentangling Style and Content in Motion with Residual Quantized Representations](https://mlsanity.com/paper/2602.02334/) (2026)
- [RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation](https://mlsanity.com/paper/2605.26241/) (2026)
- [Gaussian See, Gaussian Do: Semantic 3D Motion Transfer from Multiview Video](https://mlsanity.com/paper/2511.14848/) (2025)
- [ClusterStyle: Modeling Intra-Style Diversity with Prototypical Clustering for Stylized Motion Generation](https://mlsanity.com/paper/2512.02453/) (2025)
- [Controllable Long-term Motion Generation with Extended Joint Targets](https://mlsanity.com/paper/2512.04487/) (2025)
- [MultiAct: Text-to-Motion Generation from Composite Text via Tailored Attention Guidance](https://mlsanity.com/paper/2605.30925/) (2026)
- [Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editing](https://mlsanity.com/paper/2608.16008/) (2026)
- [UniMate: One Unified Model to Animate Diverse Skeletons](https://mlsanity.com/paper/2609.05415/) (2026)
- [Robo-Saber: Generating and Simulating Virtual Reality Players](https://mlsanity.com/paper/2602.18319/) (2026)
- [EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation](https://mlsanity.com/paper/2604.19105/) (2026)
- [Generative Motion In-betweening by Diffusion over Continuous Implicit Representations](https://mlsanity.com/paper/2605.12778/) (2026)
- [Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation](https://mlsanity.com/paper/2605.13333/) (2026)
- [AnyAct: Towards Human Reenactment of Character Motion From Video](https://mlsanity.com/paper/2605.15497/) (2026)
- [STyMo: Fast and Controllable Few-Shot Motion Style Transfer](https://mlsanity.com/paper/2609.04500/) (2026)
- [Flexible Motion Generation from Language and Style References](https://mlsanity.com/paper/2609.08032/) (2026)

## Abstract (arXiv)

Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing? Existing diffusion-based motion editing methods overlook the profound potential of the prior embedded within the weights of pre-trained models, which enables manipulating the latent feature space; hence, they primarily center on handling the motion space. In this work, we explore the attention mechanism of pre-trained motion diffusion models. We uncover the roles and interactions of attention elements in capturing and representing intricate human motion patterns, and carefully integrate these elements to transfer a leader motion to a follower one while maintaining the nuanced characteristics of the follower, resulting in zero-shot motion transfer. Editing features associated with selected motions allows us to confront a challenge observed in prior motion diffusion approaches, which use general directives (e.g., text, music) for editing, ultimately failing to convey subtle nuances effectively. Our work is inspired by how a monkey closely imitates what it sees while maintaining its unique motion patterns; hence we call it Monkey See, Monkey Do, and dub it MoMo. Employing our technique enables accomplishing tasks such as synthesizing out-of-distribution motions, style transfer, and spatial editing. Furthermore, diffusion inversion is seldom employed for motions; as a result, editing efforts focus on generated motions, limiting the editability of real ones. MoMo harnesses motion inversion, extending its application to both real and generated motions. Experimental results show the advantage of our approach over the current art. In particular, unlike methods tailored for specific applications through training, our approach is applied at inference time, requiring no training. Our webpage is at https://monkeyseedocg.github.io.