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PoseScript: Linking 3D Human Poses and Natural Language.

Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas

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    We introduce PoseScript, a dataset pairing 3D human poses with natural language descriptions. This dataset enables advanced computer vision tasks by linking pose data with rich semantic information.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Human Pose Estimation

    Background:

    • Natural language is crucial for computer vision tasks like image captioning.
    • Current 3D human pose datasets lack detailed language descriptions, hindering understanding.
    • Human pose is vital for human comprehension.

    Purpose of the Study:

    • Introduce the PoseScript dataset to bridge the gap between 3D human poses and natural language descriptions.
    • Expand the dataset size for data-hungry learning algorithms through automatic captioning.
    • Demonstrate the utility of annotated poses in multi-modal learning tasks.

    Main Methods:

    • Created PoseScript by pairing over 6,000 3D human poses (from AMASS) with human-annotated descriptions.
    • Developed an automatic captioning process using "posecodes" extracted from 3D keypoints and syntactic rules.
    • Generated 100,000 synthetic descriptions to scale the dataset for pretraining deep models.

    Main Results:

    • Established a joint embedding space for 3D poses and text, enabling cross-modal retrieval.
    • Developed a baseline for text-conditioned 3D pose generation.
    • Presented a learned process for generating pose descriptions from 3D keypoints.

    Conclusions:

    • The PoseScript dataset significantly enhances multi-modal learning for computer vision tasks.
    • Annotated 3D poses offer versatility and usefulness in various applications.
    • The dataset and methods pave the way for future research in language-guided human pose understanding.