Temporal diffuser: Timing scale-aware modulation for sign language production
Bibliographic record
Abstract
Recent advances in Sign Language Production (SLP) highlight denoising diffusion models as promising alternatives to traditional autoregressive methods. Most existing approaches follow a two-stage pipeline that encodes sign motion into discrete latent codes, often sacrificing Space–Time fidelity and requiring gloss annotations or complex codebooks. Transformer-based models aim to simplify this, but often produce overly smooth, unnatural motions. We introduce Sign Language Production with Scale-Aware Modulation (SignSAM), a novel single-stage, gloss-free SLP framework that directly synthesizes motion in continuous space, preserving fine temporal details. At its core is a Space–Time U-Net that learns compact temporal features by jointly downscaling the frame and sign feature dimensions, thereby reducing computational cost compared to a no-pyramid UNet or a pyramid UNet without consistency between dimensions. To further enhance temporal precision, we propose a Timing Scale-Aware Modulation module that fuses multiscale temporal resolutions for better motion coherence. Experiments on PHOENIX14T and How2Sign show that SignSAM achieves state-of-the-art (SOTA) fluency, accuracy, and naturalness, offering an efficient and expressive solution for SLP. Our project homepage is https://kha-kim-thuy.github.io/SLP-Demo/ .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".