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Say Anything with Any Style
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Proposes SAAS, a stylized talking-head method that quantizes speaking style via a multi-task VQ-VAE codebook and uses a HyperStyle-modulated style branch plus a pose codebook for audio- and style-conditioned head motion.
Problem
Generating talking-head videos with diverse, stylized facial expressions and head poses that match a style reference clip while keeping lip sync, avoiding averaged regressive style extraction and universal networks for all styles.
Approach
Treat style extraction as discrete codebook querying (multi-task VQ-VAE with reconstruction and style-classification auxiliary tasks), use a residual canonical + style-specific branch whose weights are offset by a HyperStyle generated from the style code, and add a pose codebook sampled by a cross-modal pose generator.
Key results
- On MEAD, SAAS reaches SSIM 0.683, FID 59.718, M-LMD 3.104 and F-LMD 2.914, the best among compared methods including StyleTalk (FID 68.399).
- On HDTF, SAAS achieves SSIM 0.732 and the lowest M-LMD (2.588), while SadTalker has the best FID (18.317 vs 18.919 for SAAS).
- In a 20-participant user study, SAAS scores 3.58 lip-sync, 3.28 naturalness and 71.3% style accuracy, above StyleTalk's 63.4%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SAAS instead of StyleTalk | stylized talking-face generation · MEAD | FID: 68.399 -> 59.718 | Helped |
| Use SAAS instead of StyleTalk | stylized talking-face generation · MEAD | F-LMD: 3.262 -> 2.914 | Helped |
| Use SAAS instead of StyleTalk | stylized talking-face generation · HDTF | Sync_conf: 2.445 -> 2.717 | Hurt |
| Use SAAS-V for video-driven style transfer | SAAS · HDTF | FID: 18.919 -> 9.057 | Helped |
| Add the discrete style codebook instead of regressive style extraction (ablation) | SAAS · MEAD | FID: 84.149 -> 59.718 | Helped |
| Set the style codebook size to 250 (ablation) | SAAS · MEAD | FID: 59.718 -> 70.151 | Hurt |
| Set the style codebook size to 750 (ablation) | SAAS · MEAD | FID: 59.718 -> 64.481 | Hurt |
| Add HyperStyle to generate style-specific weights (ablation) | SAAS · MEAD | FID: 81.415 -> 59.718 | Helped |
| Add L_trip (ablation) | SAAS · MEAD | FID: 75.045 -> 59.718 | Helped |
| Add L_style1 (ablation) | SAAS · MEAD | FID: 63.746 -> 59.718 | Helped |
| Add L_style2 (ablation) | SAAS · MEAD | FID: 67.418 -> 59.718 | Helped |
Surprising findings
- Wav2Lip achieves a higher SyncNet confidence (5.077 on HDTF) than the ground truth, which the authors attribute to SyncNet loss used in its training.
- SAAS has a slightly worse SyncNet confidence than Wav2Lip and PC-AVS on HDTF (2.717 vs 5.077 and 3.661).
Limitations
- Style-specific branch and HyperStyle depend on a style classifier and labeled style classes in training data.
- Evaluation of style-transfer (SAAS-V) relies on self-constructed proxy metrics since no ground-truth stylized video exists.
- Sync confidence on HDTF is below several baselines such as Wav2Lip.
Experimental setup
Datasets: MEAD HDTF
Benchmarks: MEAD HDTF
Metrics: SSIM FID M-LMD F-LMD Sync_conf (SyncNet) user study score style accuracy
Baselines: MakeItTalk Wav2Lip Audio2Head PC-AVS AVCT SadTalker EAMM StyleTalk
Builds on: VQ-VAE HyperNetwork StyleTalk SadTalker
Closest papers by meaning
- Style2Talker: High-Resolution Talking Head Generation with Emotion Style and Art Style (AAAI 2024 · similarity 0.90)
- OmniTalker: One-shot Real-time Text-Driven Talking Audio-Video Generation With Multimodal Style Mimicking (NeurIPS 2025 · similarity 0.87)
- AnyTalk: Multi-modal Driven Multi-domain Talking Head Generation (AAAI 2025 · similarity 0.87)
- Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation (CVPR 2025 · similarity 0.86)
- GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expressions (AAAI 2025 · similarity 0.86)
- Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation (AAAI 2024 · similarity 0.86)
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