# AStF: Motion Style Transfer via Adaptive Statistics Fusor

arXiv: https://arxiv.org/abs/2511.04192 · HTML: https://mlsanity.com/paper/2511.04192/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [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)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [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)
- [MotionFix: Text-Driven 3D Human Motion Editing](https://mlsanity.com/paper/2408.00712/) (2024)
- [PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection](https://mlsanity.com/paper/2502.12119/) (2025)
- [LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering](https://mlsanity.com/paper/2412.12359/) (2024)
- [CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process](https://mlsanity.com/paper/2505.13408/) (2025)
- [Generative Human Motion Stylization in Latent Space](https://mlsanity.com/paper/2401.13505/) (2024)
- [Unimotion: Unifying 3D Human Motion Synthesis and Understanding](https://mlsanity.com/paper/2409.15904/) (2024)
- [A Survey on Deep Learning for Skeleton-Based Human Animation](https://mlsanity.com/paper/2110.06901/) (2021)
- [MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls](https://mlsanity.com/paper/2407.21136/) (2024)
- [Light-T2M: A Lightweight and Fast Model for Text-to-motion Generation](https://mlsanity.com/paper/2412.11193/) (2024)
- [MoST: Motion Style Transformer between Diverse Action Contents](https://mlsanity.com/paper/2403.06225/) (2024)
- [All-to-key Attention for Arbitrary Style Transfer](https://mlsanity.com/paper/2212.04105/) (2022)
- [Generative Motion Stylization of Cross-structure Characters within Canonical Motion Space](https://mlsanity.com/paper/2403.11469/) (2024)
- [CigTime: Corrective Instruction Generation Through Inverse Motion Editing](https://mlsanity.com/paper/2412.05460/) (2024)
- [PackDiT: Joint Human Motion and Text Generation via Mutual Prompting](https://mlsanity.com/paper/2501.16551/) (2025)

## Built on by (most cited first, 3 of 3)

- [PersonaGesture: Single-Reference Co-Speech Gesture Personalization for Unseen Speakers](https://mlsanity.com/paper/2605.06064/) (2026)
- [Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning](https://mlsanity.com/paper/2601.21904/) (2026)
- [StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring](https://mlsanity.com/paper/2608.01643/) (2026)

## Abstract (arXiv)

Human motion style transfer allows characters to appear less rigidity and more realism with specific style. Traditional arbitrary image style transfer typically process mean and variance which is proved effective. Meanwhile, similar methods have been adapted for motion style transfer. However, due to the fundamental differences between images and motion, relying on mean and variance is insufficient to fully capture the complex dynamic patterns and spatiotemporal coherence properties of motion data. Building upon this, our key insight is to bring two more coefficient, skewness and kurtosis, into the analysis of motion style. Specifically, we propose a novel Adaptive Statistics Fusor (AStF) which consists of Style Disentanglement Module (SDM) and High-Order Multi-Statistics Attention (HOS-Attn). We trained our AStF in conjunction with a Motion Consistency Regularization (MCR) discriminator. Experimental results show that, by providing a more comprehensive model of the spatiotemporal statistical patterns inherent in dynamic styles, our proposed AStF shows proficiency superiority in motion style transfers over state-of-the-arts. Our code and model are available at https://github.com/CHMimilanlan/AStF.