BEAM: Bridging Physically-based Rendering and Gaussian Modeling for Relightable Volumetric Video
Yu Hong, Yize Wu, Zhehao Shen, Chengcheng Guo, Yuheng Jiang, Yingliang Zhang, Jingyi Yu, Lan Xu

TL;DR
BEAM introduces a pipeline that combines Gaussian representations with physically-based rendering to produce high-quality, relightable 3D volumetric videos from multi-view footage, enabling realistic lighting effects in immersive applications.
Contribution
It presents a novel method that bridges Gaussian modeling with PBR to enhance relightable volumetric video quality and efficiency from multi-view RGB data.
Findings
Achieves spatially and temporally consistent geometry recovery.
Enables real-time and offline relighting of volumetric videos.
Supports seamless integration into traditional CG pipelines.
Abstract
Volumetric video enables immersive experiences by capturing dynamic 3D scenes, enabling diverse applications for virtual reality, education, and telepresence. However, traditional methods struggle with fixed lighting conditions, while neural approaches face trade-offs in efficiency, quality, or adaptability for relightable scenarios. To address these limitations, we present BEAM, a novel pipeline that bridges 4D Gaussian representations with physically-based rendering (PBR) to produce high-quality, relightable volumetric videos from multi-view RGB footage. BEAM recovers detailed geometry and PBR properties via a series of available Gaussian-based techniques. It first combines Gaussian-based human performance tracking with geometry-aware rasterization in a coarse-to-fine optimization framework to recover spatially and temporally consistent geometries. We further enhance Gaussian…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Advanced Data Storage Technologies
MethodsDiffusion · Artemisinin Optimization based on Malaria Therapy: Algorithm and Applications to Medical Image Segmentation · Balanced Selection
