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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

870
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
870

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Related Experiment Video

Updated: Jan 12, 2026

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Consistent 3D Human Reconstruction From Monocular Video: Learning Correctable Appearance and Temporal Motion Priors.

Cheng Shang, Liang An, TingTing Li

    IEEE Transactions on Visualization and Computer Graphics
    |October 31, 2025
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    Summary

    This study introduces novel methods for rendering dynamic humans from monocular video, improving motion realism and frame continuity. The approach enhances digital human representation for more accurate viewpoint rendering.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Recent progress in rendering dynamic humans using Neural Radiance Fields (NeRF) and 3D Gaussian Splatting has advanced digital human creation.
    • Monocular video rendering faces challenges with viewpoint imbalance, affecting subtle motion rendering and temporal continuity from novel viewpoints.

    Purpose of the Study:

    • To address limitations in monocular dynamic human rendering, specifically improving motion subtlety and inter-frame continuity.
    • To enhance the accuracy and realism of digital humans rendered from various perspectives.

    Main Methods:

    • A pixel-level motion correction module was developed to rectify representation errors across different viewpoints.
    • A temporal information-based model was introduced to enhance motion continuity by utilizing adjacent video frames.

    Main Results:

    • The proposed method demonstrates superior performance in dynamic human rendering compared to existing state-of-the-art techniques.
    • Quantitative and qualitative evaluations on benchmark datasets (NeuMan, ZJU-Mocap, People-Snapshot) confirm the effectiveness of the approach.

    Conclusions:

    • The developed techniques significantly improve the rendering of dynamic humans from monocular video, particularly in handling complex motion and maintaining temporal consistency.
    • This work advances the capabilities of creating realistic and continuous digital humans from limited visual input.