# MoST: Motion Style Transformer between Diverse Action Contents

arXiv: https://arxiv.org/abs/2403.06225 · HTML: https://mlsanity.com/paper/2403.06225/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 10 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [Contact and Human Dynamics from Monocular Video](https://mlsanity.com/paper/2007.11678/) (2020)
- [Unpaired Motion Style Transfer from Video to Animation](https://mlsanity.com/paper/2005.05751/) (2020)
- [Motion Puzzle: Arbitrary Motion Style Transfer by Body Part](https://mlsanity.com/paper/2202.05274/) (2022)
- [Global-local Motion Transformer for Unsupervised Skeleton-based Action Learning](https://mlsanity.com/paper/2207.06101/) (2022)
- [RSMT: Real-time Stylized Motion Transition for Characters](https://mlsanity.com/paper/2306.11970/) (2023)
- [Style-ERD: Responsive and Coherent Online Motion Style Transfer](https://mlsanity.com/paper/2203.02574/) (2022)
- [Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2212.08526/) (2022)

## Built on by (most cited first, 18 of 18)

- [Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer](https://mlsanity.com/paper/2406.06508/) (2024)
- [PersonaBooth: Personalized Text-to-Motion Generation](https://mlsanity.com/paper/2503.07390/) (2025)
- [StyleMotif: Multi-Modal Motion Stylization using Style-Content Cross Fusion](https://mlsanity.com/paper/2503.21775/) (2025)
- [SMooGPT: Stylized Motion Generation using Large Language Models](https://mlsanity.com/paper/2509.04058/) (2025)
- [AStF: Motion Style Transfer via Adaptive Statistics Fusor](https://mlsanity.com/paper/2511.04192/) (2025)
- [PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction](https://mlsanity.com/paper/2604.08125/) (2026)
- [MulSMo: Multimodal Stylized Motion Generation by Bidirectional Control Flow](https://mlsanity.com/paper/2412.09901/) (2024)
- [Behave Your Motion: Habit-preserved Cross-category Animal Motion Transfer](https://mlsanity.com/paper/2507.07394/) (2025)
- [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)
- [ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos](https://mlsanity.com/paper/2604.10466/) (2026)
- [SMCD: High Realism Motion Style Transfer via Mamba-based Diffusion](https://mlsanity.com/paper/2405.02844/) (2024)
- [PersonaAnimator: Personalized Motion Transfer from Unconstrained Videos](https://mlsanity.com/paper/2508.19895/) (2025)
- [Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model](https://mlsanity.com/paper/2603.08023/) (2026)
- [MoSAIC: Aligned Intervention Supervision for Part-Local Motion Style Transfer](https://mlsanity.com/paper/2607.26304/) (2026)
- [Flexible Motion Generation from Language and Style References](https://mlsanity.com/paper/2609.08032/) (2026)
- [Motion Style Slider: Endpoint-Supervised Continuous Style Control for Human Motion Diffusion](https://mlsanity.com/paper/2609.30795/) (2026)

## Abstract (arXiv)

While existing motion style transfer methods are effective between two motions with identical content, their performance significantly diminishes when transferring style between motions with different contents. This challenge lies in the lack of clear separation between content and style of a motion. To tackle this challenge, we propose a novel motion style transformer that effectively disentangles style from content and generates a plausible motion with transferred style from a source motion. Our distinctive approach to achieving the goal of disentanglement is twofold: (1) a new architecture for motion style transformer with `part-attentive style modulator across body parts' and `Siamese encoders that encode style and content features separately'; (2) style disentanglement loss. Our method outperforms existing methods and demonstrates exceptionally high quality, particularly in motion pairs with different contents, without the need for heuristic post-processing. Codes are available at https://github.com/Boeun-Kim/MoST.