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About this paper
Zhaoyi An, Sihan Tan, Youngbae Hwang, Kazuhiro Nakadai, Rei Kawakami
SignFLIP connects sign-to-text translation and text-to-sign generation through a shared representation refined in stages. It reconstructs and aligns pose-keypoint features, trains for translation, then uses text features to generate pose sequences. On CSL-Daily, adding SLT training lowers generation DTW from 0.030 to 0.025; this ablation supports a benefit in that setup, not a universal effect.
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