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Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization
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Proposes SignViP, which generates sign language videos from spoken text by translating text into discrete FSQ tokens of multiple fine-grained conditions (poses and 3D hands) and injecting the decoded embeddings into a diffusion video model.
Problem
Sign language video generation (SLVG) from spoken text is limited by single coarse intermediary conditions (skeletons), hurting naturalness and expressiveness; directly predicting high-dimensional multiple fine-grained conditions is error-prone.
Approach
Replace direct high-dimensional condition prediction with a discrete multi-condition token space: a diffusion model with a multi-condition encoder produces continuous embeddings, an FSQ autoencoder compresses and quantizes them into tokens, and a frame-level autoregressive GPT-2 translator maps CLIP text embeddings to these tokens.
Key results
- SignViP reaches video back-translation BLEU-4 of 8.65 on RWTH-2014T and 5.04 on How2Sign, versus 3.16 for SignGAN and 0.51 for SignGen on RWTH-2014T.
- On RWTH-2014T, SignViP achieves FVD 1025.45 and IDS 0.571 in video quality, compared with 1431.38 and 0.463 for SignGAN.
- In a non-expert human study, SignViP was chosen as most similar to ground truth in 70.0% of RWTH-2014T votes and 81.8% of How2Sign votes.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use SignViP instead of the strongest listed baseline | SignViP · RWTH-2014T video back-translation | BLEU-4: 3.61 -> 8.65 | Helped |
| use SignViP instead of the strongest listed baseline | SignViP · How2Sign video back-translation | BLEU-4: 2.31 -> 5.04 | Helped |
| use SignViP instead of the strongest listed baseline | SignViP · RWTH-2014T video quality | FVD: 1330.54 -> 1025.45 | Helped |
| use SignViP instead of the strongest listed baseline | SignViP · How2Sign video quality | FVD: 2484.39 -> 2207.67 | Helped |
| add 3D hands (ablation) | SignViP · RWTH-2014T | FVD: 382.64 -> 275.22 | Helped |
| add fine-grained poses (ablation) | SignViP · RWTH-2014T | FVD: 461.98 -> 275.22 | Helped |
| use pretrained parameter initialization (ablation) | SignViP · RWTH-2014T video quality | FID: 2278.23 -> 508.91 | Helped |
| compress conditions rather than use no compression (ablation) | SignViP · RWTH-2014T token translation | BLEU-4: 1.32 -> 8.65 | Helped |
| use FSQ quantization instead of continuous embedding prediction (ablation) | SignViP · Sign language video generation | video quality | Helped |
| set condition augmentation probability to 10^-3 rather than none (ablation) | SignViP · Sign language video generation | FVD | Helped |
| increase condition augmentation probability beyond 10^-3 (ablation) | SignViP · Sign language video generation | video quality | Hurt |
| set scheduled sampling replacement rate to 1.0 instead of 0.4 (ablation) | SignViP · RWTH-2014T token translation | BLEU-4: 8.65 -> 5.89 | Hurt |
Surprising findings
- Direct prediction of multi-condition embeddings without quantization performs poorly, with weak semantic alignment and low video quality.
- SignGen is worse than the single-condition SignGAN baseline on back-translation metrics despite using multiple conditions.
Limitations
- Requires training a separate model for each sign language; cannot handle multiple sign languages simultaneously.
- Iterative diffusion sampling is computationally costly; acceleration methods are not explored.
- Evaluation relies on back-translation models and a small non-expert human study, which may introduce bias.
Experimental setup
Datasets: RWTH-2014T How2Sign
Benchmarks: RWTH-2014T How2Sign
Metrics: BLEU ROUGE-L COMET FID CLIP-FID FVD IDS PSNR SSIM LPIPS
Baselines: SignGAN SignGen MoMP ProTran ControlNet AnimateAnyone
Builds on: latent diffusion models finite scalar quantization AnimateAnyone Stable Diffusion v1.5 GPT-2
Closest papers by meaning
- Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observations (CVPR 2025 · similarity 0.90)
- LLMs are Good Sign Language Translators (CVPR 2024 · similarity 0.89)
- Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment (AAAI 2024 · similarity 0.89)
- Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation (NeurIPS 2025 · similarity 0.89)
- Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment (NeurIPS 2026 · similarity 0.88)
- Scaling Sign Language Translation (NeurIPS 2024 · similarity 0.88)
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