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InstaHMR: Instance-Aware One-Stage Multi-Person Human Mesh Recovery.

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    Summary

    This study introduces InstaHMR, a novel network for accurate 3D human mesh recovery from images. It enhances single-person features using Contextual Instance Guidance (CIG) and auxiliary losses, achieving state-of-the-art results on multiple datasets.

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    Area of Science:

    • Computer Vision and Machine Learning
    • 3D Human Pose and Shape Estimation

    Background:

    • Estimating 3D human meshes from images is crucial for various applications.
    • Existing one-stage methods often struggle with accurate mesh representation, especially in multi-person scenarios.

    Purpose of the Study:

    • To propose an Instance-aware Multi-person 3D Human Mesh Recovery (InstaHMR) network.
    • To improve the accuracy of 3D human mesh recovery by exploiting instance-aware features.

    Main Methods:

    • Developed a one-stage framework incorporating an instance-aware approach.
    • Introduced the Contextual Instance Guidance (CIG) module using spatial and channel attention for instance-specific features.
    • Implemented two auxiliary losses: Human Triplet Planes (HTP) loss for joint position accuracy and T-pose Shape (TS) loss for abstract shape learning.

    Main Results:

    • The proposed CIG module effectively preserves instance-specific information, outperforming pixel-level features.
    • Auxiliary losses contribute to capturing subtle joint differences and learning shape parameters.
    • Achieved state-of-the-art performance on four benchmark multi-person datasets.

    Conclusions:

    • InstaHMR demonstrates superior performance in multi-person 3D human mesh recovery.
    • The instance-aware feature representation and novel loss functions are key to the model's success.