arXiv Computer Vision
Semantic Residual Vector Quantization for Sign Language Generation
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SeRV: Semantic-Aligned Residual Vector Quantization for American Sign Language Generation
arXiv:2609.05742v1 Announce Type: cross Abstract: American Sign Language (ASL) generation remains challenging due to limited paired text-ASL motion data and the difficulty of learning motion represen...
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SignRR: Retrieve and Refine Real Motion for Sign Language Production
SignRR is a new sign language production framework that combines retrieval of real sign motion segments with a learned refinement step to produce globally coherent signing sequences. It starts from a dictionary of authentic sign segments and refines them using a part-aware Residual VQ‑VAE, preserving fine hand articulation while handling temporal length differences in latent space. Experiments on PHOENIX14T and CSL‑Daily demonstrate state‑of‑the‑art back‑translation performance and competitive pose quality.
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Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production
arXiv:2608. 09045v1 Announce Type: cross Abstract: Recent advances in sign language (SL) research have shown a trend toward unifying multiple sign language understanding (SLU) subtasks, such as isolated sign language recognition (ISLR), continuous sign language recognition (CSLR), and sign language translation (SLT), within a single framework, leading to substantial progress.
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SignMimic: Robust High-Quality Sign Language Motion Generation via Human-Shape-Oblivious Pose Transfer Guidance
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