Dimensions overall score 7.0

• The framework's capacity to synthesize coherent multi-sign sequences remains limited, restricting sentence-level generation.limitation 85%
• The Trainable Sliding Tile Attention (T-STA) mechanism accelerates inference by exploiting spatio-temporal locality patterns.technical 85%
• The proposed FlashSign framework increases video generation speed by 3.07x compared to the baseline WanX2.1-14B model.result 95%
• The FlashSign method eliminates the need for intermediate pose representations by directly mapping natural language text to sign language videos.method 90%
• The model achieves a Fréchet Video Distance (FVD) score of 453, which is the best among compared methods.result 90%
• The method uses the WLASL dataset, which contains 21,083 videos of 2,000 distinct ASL glosses performed by at least three different signers.method 95%
• T-STA integrates trainable sparsity into both training and inference, eliminating the train-test gap present in previous training-free approaches.technical 80%
• The model achieves a PSNR of 25.74 and SSIM of 0.83 with a tile size of 24x32x32, indicating high generation quality.result 90%

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