遮蔽回復を伴う局所・全体人体動作の長期予測
Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery
非自己回帰型トランスフォーマーを用いて、局所的なポーズ予測と空間内の全体動作予測を同時に行い、遮蔽による欠損関節の回復も可能にする人体動作予測手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Qiaoyue Yang, Sven Heutger, Christopher Niemann, Magnus Jung, Ayoub Al-Hamadi, Sven Wachsmuth
分類: cs.CV
原文アブストラクト
Human motion describes the three-dimensional full-body movement of a person. Anticipating such motion holds significant relevance across a wide range of application domains such as human-robot interaction, autonomous driving, animation, and healthcare. In recent research, spatial and temporal dependencies are modeled by bidirectional attention mechanisms. These typically anticipate human motion in an autoregressive manner which could cause an accumulation of errors over time. As a consequence, they solely focus on local pose forecasting. To address these limitations, we propose a non-autoregressive transformer based on spatio-temporal attention, and train it not only for local pose anticipation, but also for global motion prediction in space. Furthermore, to enhance its applicability in real-world scenarios, our model is also trained to recover missing joints due to occlusions, and is capable of processing varying lengths of history observations. Our code is publicly available at https://github.com/Q-Y-Yang/Prediction-of-Local-and-Global-Human-Motion.
