# Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion

arXiv: https://arxiv.org/abs/2503.19557 · HTML: https://mlsanity.com/paper/2503.19557/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 72 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 30 of 61)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [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)
- [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)
- [MoMask: Generative Masked Modeling of 3D Human Motions](https://mlsanity.com/paper/2312.00063/) (2023)
- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [BABEL: Bodies, Action and Behavior with English Labels](https://mlsanity.com/paper/2106.09696/) (2021)
- [Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models](https://mlsanity.com/paper/2305.18292/) (2023)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)

## Built on by (most cited first, 16 of 16)

- [HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization](https://mlsanity.com/paper/2506.15625/) (2025)
- [UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors](https://mlsanity.com/paper/2603.15975/) (2026)
- [SMooGPT: Stylized Motion Generation using Large Language Models](https://mlsanity.com/paper/2509.04058/) (2025)
- [RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation](https://mlsanity.com/paper/2605.26241/) (2026)
- [ClusterStyle: Modeling Intra-Style Diversity with Prototypical Clustering for Stylized Motion Generation](https://mlsanity.com/paper/2512.02453/) (2025)
- [IAM: Identity-Aware Human Motion and Shape Joint Generation](https://mlsanity.com/paper/2604.25164/) (2026)
- [UniMate: One Unified Model to Animate Diverse Skeletons](https://mlsanity.com/paper/2609.05415/) (2026)
- [SceneAdapt: Scene-aware Adaptation of Human Motion Diffusion](https://mlsanity.com/paper/2510.13044/) (2025)
- [Kinetic Mining in Context: Few-Shot Action Synthesis via Text-to-Motion Distillation](https://mlsanity.com/paper/2512.11654/) (2025)
- [Robo-Saber: Generating and Simulating Virtual Reality Players](https://mlsanity.com/paper/2602.18319/) (2026)
- [Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation](https://mlsanity.com/paper/2605.13333/) (2026)
- [EMA: Effort Metric Attention for Anatomical Effort-Guided Human Motion Diffusion](https://mlsanity.com/paper/2605.24566/) (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)
- [VISTA: Video-Injected Stylized Text-to-Animation](https://mlsanity.com/paper/2609.23817/) (2026)
- [Motion Style Slider: Endpoint-Supervised Continuous Style Control for Human Motion Diffusion](https://mlsanity.com/paper/2609.30795/) (2026)

## Abstract (arXiv)

Text-to-motion generative models span a wide range of 3D human actions but struggle with nuanced stylistic attributes such as a "Chicken" style. Due to the scarcity of style-specific data, existing approaches pull the generative prior towards a reference style, which often results in out-of-distribution low quality generations. In this work, we introduce LoRA-MDM, a lightweight framework for motion stylization that generalizes to complex actions while maintaining editability. Our key insight is that adapting the generative prior to include the style, while preserving its overall distribution, is more effective than modifying each individual motion during generation. Building on this idea, LoRA-MDM learns to adapt the prior to include the reference style using only a few samples. The style can then be used in the context of different textual prompts for generation. The low-rank adaptation shifts the motion manifold in a semantically meaningful way, enabling realistic style infusion even for actions not present in the reference samples. Moreover, preserving the distribution structure enables advanced operations such as style blending and motion editing. We compare LoRA-MDM to state-of-the-art stylized motion generation methods and demonstrate a favorable balance between text fidelity and style consistency.