# UnifiedGesture: A Unified Gesture Synthesis Model for Multiple Skeletons

arXiv: https://arxiv.org/abs/2309.07051 · HTML: https://mlsanity.com/paper/2309.07051/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 68 of its 110 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 30 of 68)

- [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)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Efficient Content-Based Sparse Attention with Routing Transformers](https://mlsanity.com/paper/2003.05997/) (2020)
- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [FLAME: Free-form Language-based Motion Synthesis & Editing](https://mlsanity.com/paper/2209.00349/) (2022)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity](https://mlsanity.com/paper/2009.02119/) (2020)
- [Learning Individual Styles of Conversational Gesture](https://mlsanity.com/paper/1906.04160/) (2019)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis](https://mlsanity.com/paper/2203.05297/) (2022)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents](https://mlsanity.com/paper/2303.14613/) (2023)
- [Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation](https://mlsanity.com/paper/2303.09119/) (2023)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots](https://mlsanity.com/paper/1810.12541/) (2018)
- [Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders](https://mlsanity.com/paper/2108.06720/) (2021)

## Built on by (most cited first, 15 of 15)

- [MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models](https://mlsanity.com/paper/2403.09471/) (2024)
- [Enabling Synergistic Full-Body Control in Prompt-Based Co-Speech Motion Generation](https://mlsanity.com/paper/2410.00464/) (2024)
- [SemTalk: Holistic Co-speech Motion Generation with Frame-level Semantic Emphasis](https://mlsanity.com/paper/2412.16563/) (2024)
- [MotionScript: Natural Language Descriptions for Expressive 3D Human Motions](https://mlsanity.com/paper/2312.12634/) (2023)
- [Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness](https://mlsanity.com/paper/2401.03476/) (2024)
- [MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation](https://mlsanity.com/paper/2408.03312/) (2024)
- [DyaDiT: A Multi-Modal Diffusion Transformer for Socially Favorable Dyadic Gesture Generation](https://mlsanity.com/paper/2602.23165/) (2026)
- [Speech-driven Personalized Gesture Synthetics: Harnessing Automatic Fuzzy Feature Inference](https://mlsanity.com/paper/2403.10805/) (2024)
- [Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis](https://mlsanity.com/paper/2504.09885/) (2025)
- [CoordSpeaker: Exploiting Gesture Captioning for Coordinated Caption-Empowered Co-Speech Gesture Generation](https://mlsanity.com/paper/2511.22863/) (2025)
- [DiM-Gesture: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 framework](https://mlsanity.com/paper/2408.00370/) (2024)
- [Allo-AVA: A Large-Scale Multimodal Conversational AI Dataset for Allocentric Avatar Gesture Animation](https://mlsanity.com/paper/2410.16503/) (2024)
- [EchoAvatar: Real-time Generative Avatar Animation from Audio Streams](https://mlsanity.com/paper/2605.28272/) (2026)
- [Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication](https://mlsanity.com/paper/2505.04996/) (2025)
- [Towards Unified Co-Speech Gesture Generation via Hierarchical Implicit Periodicity Learning](https://mlsanity.com/paper/2512.13131/) (2025)

## Abstract (arXiv)

The automatic co-speech gesture generation draws much attention in computer animation. Previous works designed network structures on individual datasets, which resulted in a lack of data volume and generalizability across different motion capture standards. In addition, it is a challenging task due to the weak correlation between speech and gestures. To address these problems, we present UnifiedGesture, a novel diffusion model-based speech-driven gesture synthesis approach, trained on multiple gesture datasets with different skeletons. Specifically, we first present a retargeting network to learn latent homeomorphic graphs for different motion capture standards, unifying the representations of various gestures while extending the dataset. We then capture the correlation between speech and gestures based on a diffusion model architecture using cross-local attention and self-attention to generate better speech-matched and realistic gestures. To further align speech and gesture and increase diversity, we incorporate reinforcement learning on the discrete gesture units with a learned reward function. Extensive experiments show that UnifiedGesture outperforms recent approaches on speech-driven gesture generation in terms of CCA, FGD, and human-likeness. All code, pre-trained models, databases, and demos are available to the public at https://github.com/YoungSeng/UnifiedGesture.