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Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation
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Introduces emotion-transition 3D co-speech gesture generation with two newly constructed datasets (BEAT-ETrans, TED-ETrans) and a weakly supervised method using motion transition infusion, emotion mixture supervision, and a VAE keyframe sampler.
Problem
Generating long-sequence 3D co-speech gestures that transition between emotions, where no aligned 3D pose annotations exist for dynamically synthesized transition speech.
Approach
Model temporal correlation between head and tail gesture chunks as style guidance injected via AdaIN into transition features, and supervise transition gestures weakly with a learnable mixed emotion label fed to a pre-trained pose-based emotion classifier; a VAE keyframe sampler provides diverse initial poses.
Key results
- On BEAT-ETrans, the method reaches FGD_h+t 4.42 and FGD_trans 18.84, versus 6.68 and 25.03 for the best baseline DiffGesture.
- On TED-ETrans, FGD_h+t improves from 16.72 (HA2G) to 12.19, about 34.8% relative to the sub-optimal DiffGesture (18.69).
- Ablation shows the motion transition infusion mechanism raises BC on BEAT-ETrans from 0.701 to 0.866.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed method instead of DiffGesture | Co-speech gesture generation · BEAT-ETrans | FGDtrans: 25.03 -> 18.84 | Helped |
| Use the proposed method instead of DiffGesture | Co-speech gesture generation · BEAT-ETrans | BC: 0.788 -> 0.881 | Helped |
| Use the proposed method instead of DiffGesture | Co-speech gesture generation · TED-ETrans | FGDtrans: 25.13 -> 23.54 | Helped |
| Use the proposed method instead of S2G | Co-speech gesture generation · TED-ETrans | BC: 0.824 -> 0.906 | Helped |
| Add MTIM (ablation) | Proposed gesture-generation method · BEAT-ETrans | BC: 0.701 -> 0.866 | Helped |
| Add EMS to the method with MTIM (ablation) | Proposed gesture-generation method · TED-ETrans | FGDtrans: 36.38 -> 26.21 | Helped |
| Add KFS to the method with MTIM and EMS (ablation) | Proposed gesture-generation method · BEAT-ETrans | Diversity: 112.80±1.95 -> 124.93±2.10 | Helped |
| Add KFS to the method with MTIM and EMS (ablation) | Proposed gesture-generation method · TED-ETrans | Diversity: 87.21±2.12 -> 93.79±2.53 | Helped |
Surprising findings
- DiffGesture achieves strong head/tail FGD but worse transition FGD due to lack of transition supervision.
Limitations
- Transition gestures are only weakly supervised, with no 3D ground truth for transitions.
- Keyframe-wise sampling limits temporal smoothness of diverse outputs, as acknowledged by the authors.
- TED-ETrans emotion labels come from a pseudo-labeling audio classifier with a 0.95 threshold.
Experimental setup
Datasets: BEAT TED-Expressive BEAT-ETrans TED-ETrans
Benchmarks: BEAT-ETrans TED-ETrans
Metrics: FGD BC Diversity user study naturalness/smoothness/coherency
Baselines: Seq2Seq S2G Trimodal CAMN HA2G DiffGesture
Builds on: HA2G DiffGesture CAMN Trimodal AdaIN
Closest papers by meaning
- Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion (CVPR 2024 · similarity 0.90)
- Co$^{\mathbf{3}}$Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion (ICLR 2025 · similarity 0.89)
- Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control (AAAI 2024 · similarity 0.88)
- DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation (AAAI 2025 · similarity 0.87)
- Towards Variable and Coordinated Holistic Co-Speech Motion Generation (CVPR 2024 · similarity 0.87)
- The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion (CVPR 2025 · similarity 0.86)
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