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KPGS: Toward Real-World Complex Dynamic Scene Rendering With Keyframe-Driven Predictable Gaussian Splatting.

Yiqian Chang, Haoran Xu, Jianing Li

    IEEE Transactions on Visualization and Computer Graphics
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    Summary

    Keyframe-driven Predictable Gaussian Splatting (KPGS) enhances dynamic scene rendering by accurately reconstructing complex motions. This method improves view synthesis performance while balancing storage and computational costs for efficient real-world scene understanding.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Dynamic Scene Rendering (DSR) methods struggle with complex motions due to unified deformation models.
    • Temporal decomposition methods in DSR overlook motion correlations and increase storage needs.

    Purpose of the Study:

    • Introduce Keyframe-driven Predictable Gaussian Splatting (KPGS) for high-fidelity complex dynamic scene rendering.
    • Address limitations in reconstruction fidelity and efficiency of existing DSR techniques.

    Main Methods:

    • Utilize patch-wise HSV clustering for keyframe extraction.
    • Employ a Transformer-based prediction network for calculating deformable Gaussians via voxelization.
    • Implement an inter-frame deformation network with mutual supervision for temporal continuity.

    Main Results:

    • KPGS achieves higher average view synthesis performance compared to State-Of-The-Art (SOTA) approaches.
    • Demonstrates a favorable balance between storage costs and rendering performance.
    • Validated on newly built (MotionGS) and public benchmarks (HyperNeRF, Neu3D).

    Conclusions:

    • KPGS offers an efficient framework for rendering complex dynamic scenes with improved fidelity.
    • The proposed method effectively handles intricate motions and maintains temporal consistency.
    • KPGS represents a significant advancement in dynamic scene rendering technology.