# HOP: Heterogeneous Topology-based Multimodal Entanglement for Co-Speech Gesture Generation

arXiv: https://arxiv.org/abs/2503.01175 · HTML: https://mlsanity.com/paper/2503.01175/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 57 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 30 of 34)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [Attention Bottlenecks for Multimodal Fusion](https://mlsanity.com/paper/2107.00135/) (2021)
- [Language2Pose: Natural Language Grounded Pose Forecasting](https://mlsanity.com/paper/1907.01108/) (2019)
- [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)
- [BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis](https://mlsanity.com/paper/2203.05297/) (2022)
- [Monocular Expressive Body Regression through Body-Driven Attention](https://mlsanity.com/paper/2008.09062/) (2020)
- [Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation](https://mlsanity.com/paper/2303.09119/) (2023)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [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)
- [Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation](https://mlsanity.com/paper/2203.13161/) (2022)
- [EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling](https://mlsanity.com/paper/2401.00374/) (2023)
- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://mlsanity.com/paper/2106.09296/) (2021)
- [DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models](https://mlsanity.com/paper/2305.04919/) (2023)
- [Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2206.13028/) (2022)
- [A Comprehensive Review of Data-Driven Co-Speech Gesture Generation](https://mlsanity.com/paper/2301.05339/) (2023)
- [Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning](https://mlsanity.com/paper/2202.10629/) (2022)
- [Speech2AffectiveGestures: Synthesizing Co-Speech Gestures with Generative Adversarial Affective Expression Learning](https://mlsanity.com/paper/2108.00262/) (2021)
- [Audio-Driven Co-Speech Gesture Video Generation](https://mlsanity.com/paper/2212.02350/) (2022)
- [ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis](https://mlsanity.com/paper/2403.17936/) (2024)
- [The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation](https://mlsanity.com/paper/2208.10441/) (2022)
- [The GENEA Challenge 2023: A large scale evaluation of gesture generation models in monadic and dyadic settings](https://mlsanity.com/paper/2308.12646/) (2023)
- [A Review of Evaluation Practices of Gesture Generation in Embodied Conversational Agents](https://mlsanity.com/paper/2101.03769/) (2021)
- [Graph Switching Dynamical Systems](https://mlsanity.com/paper/2306.00370/) (2023)

## Built on by (most cited first, 6 of 6)

- [Low-bit Model Quantization for Deep Neural Networks: A Survey](https://mlsanity.com/paper/2505.05530/) (2025)
- [PersonaGesture: Single-Reference Co-Speech Gesture Personalization for Unseen Speakers](https://mlsanity.com/paper/2605.06064/) (2026)
- [ProAct: Harnessing Streaming Motion Generation and Agentic Reasoning for Real-Time Embodied Social Interaction](https://mlsanity.com/paper/2602.14048/) (2026)
- [PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation](https://mlsanity.com/paper/2606.19935/) (2026)
- [Towards Reliable Human Evaluations in Gesture Generation: Insights from a Community-Driven State-of-the-Art Benchmark](https://mlsanity.com/paper/2511.01233/) (2025)
- [Super Star: Towards Streaming Real-time Interactive Agents for Digital Humans](https://mlsanity.com/paper/2608.24909/) (2026)

## Abstract (arXiv)

Co-speech gestures are crucial non-verbal cues that enhance speech clarity and expressiveness in human communication, which have attracted increasing attention in multimodal research. While the existing methods have made strides in gesture accuracy, challenges remain in generating diverse and coherent gestures, as most approaches assume independence among multimodal inputs and lack explicit modeling of their interactions. In this work, we propose a novel multimodal learning method named HOP for co-speech gesture generation that captures the heterogeneous entanglement between gesture motion, audio rhythm, and text semantics, enabling the generation of coordinated gestures. By leveraging spatiotemporal graph modeling, we achieve the alignment of audio and action. Moreover, to enhance modality coherence, we build the audio-text semantic representation based on a reprogramming module, which is beneficial for cross-modality adaptation. Our approach enables the trimodal system to learn each other's features and represent them in the form of topological entanglement. Extensive experiments demonstrate that HOP achieves state-of-the-art performance, offering more natural and expressive co-speech gesture generation. More information, codes, and demos are available here: https://star-uu-wang.github.io/HOP/