论文标题
现在每个人都可以签名吗？探索2D姿势的手语视频生成
Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses
论文作者
论文摘要
最近的工作已经解决了人类关节的2D/3D坐标代表手语的人类姿势的产生。我们在深度学习中使用艺术状态进行运动转移，并在美国手语数据集2sign上对其进行评估，以生成签名者的视频，这些签名者在给定2D姿势骨架的情况下执行手语。我们对生成的视频进行了定量和定性的评估，这表明由于缺乏细节，目前的模型不足以生成足够的手语视频。
Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2D pose skeleton. We evaluate the generated videos quantitatively and qualitatively showing that the current models are not enough to generated adequate videos for Sign Language due to lack of detail in hands.
