A general framework for Gaussian Splatting-based human-centric volumetric videos

Abstract Volumetric video is revolutionizing immersive media, with 3D Gaussian Splatting emerging as a key technology due to its unprecedented real-time rendering quality. However, extending this technology to dynamic scenes presents two major challenges: the massive storage and transmission overhead associated with temporal sequences, and a fragmented toolchain ecosystem that hinders efficient research and development. Existing solutions typically focus on isolated stages such as reconstruction or compression, lacking a unified, end-to-end workflow from data acquisition to final viewing. To address these challenges, we propose a comprehensive dynamic Gaussian processing framework that provides a complete, end-to-end pipeline. This framework systematically integrates the entire process, from data acquisition and standardized preprocessing to a suite of diverse dynamic Gaussian reconstruction algorithms. One of its core contributions is a general-purpose compression framework, compatible with the outputs of various reconstruction methods, which significantly reduces the storage footprint of dynamic sequences while maintaining high visual fidelity. To ensure broad accessibility, we have also developed a cross-platform rendering solution that supports high-quality, interactive free-viewpoint experiences on desktop, mobile, and XR devices. Furthermore, to advance the field, we contribute a large-scale, high-quality dynamic human performance capture dataset. Captured with a dense 81-camera array, the dataset comprises over 130 sequences of diverse human motions, including complex interactions with topological changes. Our integrated framework and dataset aim to bridge the entire pipeline from data creation to end-user application, providing a solid foundation for the large-scale adoption and future research of Gaussian Splatting technology.