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New Transformer Model Predicts Long-Term Human Motion with Occlusion Recovery

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel non-autoregressive transformer model for predicting human motion over extended periods. This model addresses limitations of existing autoregressive methods by focusing on both local pose and global motion prediction, and by incorporating occlusion recovery for missing joint data. The proposed approach aims to improve accuracy and applicability in real-world scenarios across fields like robotics, autonomous driving, and healthcare. AI

IMPACT This research could enhance AI capabilities in applications requiring accurate long-term human motion prediction and occlusion handling.

RANK_REASON The cluster contains an academic paper detailing a new model and methodology.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Transformer Model Predicts Long-Term Human Motion with Occlusion Recovery

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The cluster contains an academic paper detailing a new model and methodology.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Qiaoyue Yang, Sven Heutger, Christopher Niemann, Magnus Jung, Ayoub Al-Hamadi, Sven Wachsmuth ·

    Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery

    arXiv:2606.27900v1 Announce Type: new Abstract: 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, animat…

  2. arXiv cs.CV TIER_1 English(EN) · Sven Wachsmuth ·

    Long-Term Prediction of Local and Global Human Motion with Occlusion Recovery

    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…

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