SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning
Sign Language Production (SLP) faces a fundamental trade-off: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionary-retrieval methods produce disjointed transitions. To resolve this, we propose a novel training paradigm that leverages sparse keyframes to capture the underlying kinematic distribution of human signing. By generating dense motion from discrete anchors, our approach mitigates regression-to-the-mean while ensuring fluid articulation. To achieve this at scale, we introduce FAST, an ultra-efficient sign segmentation model that automatically mines precise temporal boundaries. We then present SignSparK, a Conditional Flow Matching (CFM) framework ...
| Authors | Jianhe Low, Alexandre Symeonidis-Herzig, Maksym Ivashechkin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 March 2026 |
| Last updated at source | 25 June 2026 |
Models and datasets referencing this paper
- LionelLow/SignSparK_dataDataset
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