New AI paper introduces HiFi4G, a compact 4D Gaussian representation combining nonrigid tracking with Gaussian Splatting for realistic human performance rendering. The study’s dual-graph approach efficiently recovers spatially-temporally consistent 4D Gaussians with a complementary compression method, enabling immersive human performance experiences across various platforms. The research is a breakthrough in photo-real human modeling and efficient rendering.
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HiFi4G: A Breakthrough in Photo-Real Human Modeling and Efficient Rendering
Introduction
Volumetric recording and realistic representation of 4D (spacetime) human performance dissolve the barriers between spectators and performers. This offers a variety of immersive VR/AR experiences, such as telepresence and tele-education. However, early systems faced challenges such as occlusions and texture deficiencies, leading to gaps and noise in the reconstruction output. Recent neural breakthroughs, exemplified by HiFi4G, optimize a coordinate-based multi-layer perceptron (MLP) to achieve photo-realistic volume rendering.
Key Features of HiFi4G
HiFi4G introduces a compact 4D Gaussian representation that connects Gaussian Splatting with nonrigid tracking for human performance rendering. It provides a dual-graph approach that efficiently recovers spatially-temporally consistent 4D Gaussians using different regularization designs. Additionally, it offers a complementary compression approach that enables a low-storage immersive human performance experience across several platforms.
Practical Applications
With the explicit representation provided by HiFi4G, users can effortlessly integrate it into the GPU-based rasterization pipeline, allowing for high-fidelity human performances in virtual reality while wearing VR headsets. The method combines the classic non-rigid fusion technique with differentiable rasterization advancement from multi-view human performance video to effectively generate compact 4D assets.
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