Research map / Vision-language and multimodal / Cross-modal learning
HOP: Heterogeneous Topology-based Multimodal Entanglement for Co-Speech Gesture Generation
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Proposes HOP, a topology-based heterogeneous multimodal entanglement model that uses audio as a bridge via a text-audio reprogramming module and audio-action spatiotemporal graph modeling to generate co-speech gestures, evaluated on TED Gesture and TED Expressive.
Problem
Co-speech gesture generation methods treat text, audio, and gesture motion as independent inputs, producing gestures that are less diverse and coherent than desired.
Approach
Treats audio as a bridge between text and action: a reprogramming module aligns Mel-spectrogram features to LLM/word-embedding space for audio-text adaptation, and Graph-WaveNet-style spatiotemporal graph encoding with dilated TCN links audio rhythm to gesture motion, fused into a GRU generator trained with Huber, style, KLD, and GAN losses.
Key results
- On TED Gesture, HOP reaches FGD 1.406 and BC 0.762 vs. 1.506 / 0.699 for DiffGesture (Taming Diffusion).
- On TED Expressive, HOP achieves FGD 1.815 vs. 2.600 for DiffGesture, while BC (0.738) is lower than DiffGesture's 0.718 is higher; HOP's BC is 0.738 vs 0.718.
- Ablation: removing the reprogramming layer raises FGD from 1.406 to 1.721; removing the graph encoder raises FGD to 2.026.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use HOP instead of DiffGesture | Co-speech gesture generation · TED Gesture | FGD: 1.506 -> 1.406 | Helped |
| use HOP instead of DiffGesture | Co-speech gesture generation · TED Gesture | BC: 0.699 -> 0.762 | Helped |
| use HOP instead of DiffGesture | Co-speech gesture generation · TED Gesture | Diversity: 106.722 -> 108.176 | Helped |
| use HOP instead of DiffGesture | Co-speech gesture generation · TED Expressive | FGD: 2.600 -> 1.815 | Helped |
| use HOP instead of DiffGesture | Co-speech gesture generation · TED Expressive | BC: 0.718 -> 0.738 | Helped |
| use HOP instead of DiffGesture | Co-speech gesture generation · TED Expressive | Diversity: 182.757 -> 183.332 | Helped |
| replace BERT text encoding with Trimodal-style text feature extraction (ablation) | HOP · TED Gesture | FGD: 1.406 -> 1.955 | Hurt |
| replace BERT text encoding with Trimodal-style text feature extraction (ablation) | HOP · TED Gesture | BC: 0.762 -> 0.701 | Hurt |
| replace BERT text encoding with Trimodal-style text feature extraction (ablation) | HOP · TED Gesture | Diversity: 108.176 -> 105.311 | Hurt |
| use GPT-2 instead of BERT as text encoder (ablation) | HOP · TED Gesture | FGD: 1.406 -> 1.319 | Helped |
| use GPT-2 instead of BERT as text encoder (ablation) | HOP · TED Gesture | BC: 0.762 -> 0.753 | Hurt |
| use GPT-2 instead of BERT as text encoder (ablation) | HOP · TED Gesture | Diversity: 108.176 -> 107.036 | Hurt |
Surprising findings
- Using GPT-2 as the text encoder gives a slightly lower FGD (1.319) than BERT (1.406) on TED Gesture, though BERT has higher BC.
Limitations
- Human evaluation covers only 26 participants and 30 cases, a small study.
- Evaluation limited to two public datasets (TED Gesture, TED Expressive).
- The paper notes excessively high BC can yield unnatural gestures, so BC alone is not a sufficient measure.
Experimental setup
Datasets: TED Gesture TED Expressive
Benchmarks: TED Gesture TED Expressive
Metrics: FGD BC Diversity MOS user study (naturalness, smoothness, semantic relevance,
Baselines: Attention Seq2Seq Speech2Gesture Joint Embedding Trimodal HA2G Taming Diffusion (DiffGesture)
Builds on: Trimodal Graph WaveNet model reprogramming HA2G
Closest papers by meaning
- Co-Speech Gesture Video Generation with Implicit Motion-Audio Entanglement (CVPR 2025 · similarity 0.90)
- Co$^{\mathbf{3}}$Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion (ICLR 2025 · similarity 0.89)
- ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis (CVPR 2024 · similarity 0.88)
- Retrieving Semantics from the Deep: an RAG Solution for Gesture Synthesis (CVPR 2025 · similarity 0.87)
- TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation (ICLR 2025 · similarity 0.86)
- DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation (CVPR 2024 · similarity 0.86)
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