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Chain of Generation: Multi-Modal Gesture Synthesis via Cascaded Conditional Control
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Proposes Chain of Generation (CoG), a cascaded speech-to-gesture model that generates facial blendshapes, body, and hand gestures sequentially, using speech-derived facial rhythm and emotion-based style priors so no extra modalities are needed at inference.
Problem
Co-speech 3D gesture generation performs poorly when auxiliary modalities (facial blendshapes, emotion labels) are missing at inference, and retraining or capturing them is costly.
Approach
Derive multimodal priors from speech itself: a face generator predicts blendshapes from audio, a rhythmic identification (InfoNCE) loss aligns facial features with audio rhythm, and an emotion/speaker classifier drives a hypernetwork-based GestureAdaLN style injector; body and hand decoders are cascaded after these.
Key results
- CoG achieves FGD 45.87, SRGR 0.308, BeatAlign 0.931 on BEAT, versus CaMN at FGD 123.7, SRGR 0.239, BeatAlign 0.783.
- Adding the temporal facial feature decoder reduces FGD from 88.50 to 60.44 in ablation (about 31.7%).
- In a 22-participant user study, CoG scores highest among compared methods on naturalness (3.916 vs CaMN 3.588) and other dimensions.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use CoG instead of GestureDiffuCLIP | BEAT gesture synthesis · BEAT test | FGD: 85.17 -> 45.87 | Helped |
| use CoG instead of CaMN | BEAT gesture synthesis · BEAT test | SRGR: 0.239 -> 0.308 | Helped |
| use CoG instead of TalkShow | BEAT gesture synthesis · BEAT test | BeatAlign: 0.840 -> 0.931 | Helped |
| add the face generator and temporal facial feature decoder (ablation) | CoG · BEAT test ablation | FGD: 88.50 -> 60.44 | Helped |
| add channel-wise attention (ablation) | CoG with face generator · BEAT test ablation | FGD: 60.44 -> 58.74 | Helped |
| add the emotion-aware style injector (ablation) | CoG with channel-wise attention · BEAT test ablation | BeatAlign: 0.842 -> 0.916 | Helped |
| add rhythmic identification loss (ablation) | CoG with style injector · BEAT test ablation | SRGR: 0.241 -> 0.308 | Helped |
| vary reconstruction, face reconstruction, and rhythmic loss weights (ablation) | CoG · BEAT test hyperparameter ablation | FGD: 58.67; 64.32; 56.21; 58.75; 46.31; 50.52; 48.29; 45.87 | Mixed |
| vary reconstruction, face reconstruction, and rhythmic loss weights (ablation) | CoG · BEAT test hyperparameter ablation | SRGR: 0.297; 0.290; 0.283; 0.296; 0.297; 0.295; 0.290; 0.308 | Mixed |
| vary reconstruction, face reconstruction, and rhythmic loss weights (ablation) | CoG · BEAT test hyperparameter ablation | BeatAlign: 0.924; 0.924; 0.925; 0.926; 0.925; 0.943; 0.927; 0.931 | Mixed |
Surprising findings
- Ablation on loss weights shows that the best setting uses a small rhythmic loss weight (lambda_Rhy=1) and large MSE weight, with the rhythm loss relying on facial features learned first.
Limitations
- Relies on annotated facial blendshapes during training, which require capture devices.
- Evaluated on a subset of BEAT (English speakers, upper body joints only).
Experimental setup
Datasets: BEAT
Benchmarks: BEAT
Metrics: FGD SRGR BeatAlign user study ratings (naturalness, appropriateness, correctnes
Baselines: Seq2Seq Speech2Gesture MultiContext Audio2Gesture CaMN TalkShow GestureDiffuCLIP
Builds on: CaMN wav2vec 2.0 adaptive layer normalization InfoNCE
Closest papers by meaning
- Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion (CVPR 2024 · similarity 0.89)
- ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis (CVPR 2024 · similarity 0.89)
- The Language of Motion: Unifying Verbal and Non-verbal Language of 3D Human Motion (CVPR 2025 · similarity 0.88)
- DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation (CVPR 2024 · similarity 0.88)
- Weakly-Supervised Emotion Transition Learning for Diverse 3D Co-speech Gesture Generation (CVPR 2024 · similarity 0.88)
- Co$^{\mathbf{3}}$Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion (ICLR 2025 · similarity 0.88)
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