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Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks Methods and Applications
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Builds large-scale VoxCeleb2-derived 3D facial mesh benchmarks, new probabilistic metrics, and a two-stage RVQ autoregressive model for diverse yet speech-synchronized 3D facial motion synthesis.
Problem
Speech-driven 3D facial animation is treated as deterministic one-to-one regression on small datasets, failing to capture the one-to-many distribution of facial motion; probabilistic models lack suitable large datasets and metrics.
Approach
Model the full conditional distribution of facial motion via a two-stage autoregressive transformer over coarse-to-fine residual VQ codes, with speaker style conditioning in the depth model, plus sampling strategies (KNN, code averaging, SyncNet rejection) to trade diversity for lip fidelity.
Key results
- On the benchmark, the proposed model outperforms VOCA, FaceFormer, and MeshTalk on realism/diversity (averaged FD), sync score, and coverage/mean estimate error.
- Training AVSR on the model's synthetic LRS3 pretrain meshes reduces WER to 7.1 versus 30.7 for real AV training, an 11.3% relative improvement over ground-truth meshes (WER 8.0 with real AV added) and 47% over FaceFormer synthetic meshes (13.4).
- In a perceptual study, raters preferred the proposed method over VOCA, FaceFormer, CodeTalker, and FaceFormer+Style on style matching and realism, with e.g. 85.1% vs 8.5% for style matching against VOCA.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use probabilistic speech-driven synthesis instead of FaceFormer+Style for reference-style matching | Speech-driven 3D facial motion synthesis · DECA VoxCeleb2 | Style Cosine Similarity: 0.127 -> 0.707 | Helped |
| Use probabilistic speech-driven synthesis instead of MeshTalk-ND+Style for reference-style matching | Speech-driven 3D facial motion synthesis · SPECTRE VoxCeleb2 | Style Rank: 38.8 -> 20.5 | Helped |
| Use probabilistic speech-driven synthesis instead of FaceFormer+Style | Speech-driven 3D facial motion synthesis · DECA VoxCeleb2 | Style-FD: 58.652 -> 21.038 | Hurt |
| Replace autoregressive transformer with a convolutional architecture (ablation) | Probabilistic autoregressive model · Speech-driven facial motion synthesis | Sync Score: 0.4634 -> 0.217 | Hurt |
| Move style conditioning early into the temporal autoregressive model (ablation) | Probabilistic autoregressive model · Speech-driven facial motion synthesis | Sync Score: 0.4634 -> 0.287 | Hurt |
| Train the AVSR model with meshes generated by Ours instead of ground-truth visual data | Noisy audio-visual speech recognition model · LRS3 test | WER: 30.7 -> 7.1 | Helped |
| Train the AVSR model with meshes generated by Ours instead of FaceFormer | Noisy audio-visual speech recognition model · LRS3 test | WER: 13.4 -> 7.1 | Helped |
Surprising findings
- Lip vertex error penalizes diversity, so deterministic methods (VOCA, FaceFormer) score better on it even though the probabilistic model matches ground-truth lip motion better in coverage and mean-estimate error.
- Convolutional autoregressive modeling (as in MeshTalk) and placing speaker style in the temporal model both significantly hurt speech synchronization.
Limitations
- Ground-truth meshes come from monocular reconstruction (DECA, SPECTRE) and are noisier than multi-view captures.
- Diffusion-based comparison (FaceDiffuser) was only preliminary, trained on the authors' data.
- Lip vertex error penalizes the diversity of probabilistic samples, so it is an imperfect metric for probabilistic models.
Experimental setup
Datasets: VoxCeleb2 VOCASet BIWI LRS3
Benchmarks: VoxCeleb2 audio-mesh benchmark (DECA and SPECTRE meshes) LRS3
Metrics: lip vertex error coverage error mean estimate error SyncNet score SyncNet-FD style cosine similarity style rank style FD WER
Baselines: VOCA FaceFormer FaceFormer+Style CodeTalker MeshTalk MeshTalk-ND FaceDiffuser
Builds on: residual vector quantization FaceFormer MeshTalk CodeTalker VOCA
Closest papers by meaning
- ProsodyTalker: 3D Visual Speech Animation via Prosody Decomposition (AAAI 2025 · similarity 0.92)
- DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation (AAAI 2025 · similarity 0.91)
- Seeing Speech: Learning Visible Articulatory Dynamics for Speech-Driven 3D Facial Animation (NeurIPS 2026 · similarity 0.90)
- Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation (AAAI 2024 · similarity 0.89)
- Perceptually Accurate 3D Talking Head Generation: New Definitions, Speech-Mesh Representation, and Evaluation Metrics (CVPR 2025 · similarity 0.89)
- FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models (CVPR 2024 · similarity 0.89)
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