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Related Experiment Video

Updated: Jul 1, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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Animatable Virtual Humans: Learning Pose-Dependent Human Representations in UV Space for Interactive Performance

Wieland Morgenstern, Milena T Bagdasarian, Anna Hilsmann

    IEEE Transactions on Visualization and Computer Graphics
    |March 11, 2024
    PubMed
    Summary
    This summary is machine-generated.

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    We introduce a new method for creating realistic virtual humans using pose-dependent appearance and geometry. This technique leverages statistical body models for efficient real-time animation and rendering in 3D applications.

    Area of Science:

    • Computer Graphics
    • Computer Vision
    • Human-Computer Interaction

    Background:

    • Realistic real-time animation and rendering of virtual humans is crucial for 3D applications.
    • Accurate dynamic mesh sequences from multiview-video reconstruction are essential for capturing human motion.
    • Learning pose-dependent appearance and geometry from mesh sequences presents significant challenges due to the complexity of human shape and motion.

    Purpose of the Study:

    • To propose a novel representation for virtual humans enabling highly realistic real-time animation and rendering.
    • To leverage statistical body models, such as SMPL (Skinned Multi-Person Linear Model), to constrain the learning search space and define pose-dependency.
    • To encode both pose-dependent appearance and geometry within the consistent UV space of the SMPL model.

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    Main Methods:

    • Utilizing highly accurate dynamic mesh sequences obtained from state-of-the-art multiview-video reconstruction.
    • Employing statistical body models (SMPL) to provide a-priori knowledge and constrain the learning process.
    • Learning the difference between observed geometry and the fitted SMPL model instead of absolute pose-dependent geometry.

    Main Results:

    • Successful encoding of pose-dependent appearance and geometry in the consistent UV space of the SMPL model.
    • Achieved a high level of realism in virtual human representation.
    • Enabled streamlined processing and rendering of virtual humans for real-time applications.

    Conclusions:

    • The proposed novel representation facilitates highly realistic and efficient real-time animation and rendering of virtual humans.
    • Leveraging statistical body models significantly enhances the learning process for pose-dependent human characteristics.
    • This approach offers a promising direction for advancing the quality and performance of virtual humans in 3D environments.