ViewGauss: A Head Movement Dataset for 6DoF Gaussian Splatting Video Viewing

摘要

Gaussian splatting video has recently emerged as a promising representation for immersive 6-degree-of-freedom (6DoF) content due to its low-latency rendering, compact data structure, and high visual fidelity. In particular, 4D Gaussian splatting video-which models dynamic scenes as temporally evolving Gaussian splats in 3D space-offers an efficient solution for rendering photorealistic, interactive experiences. However, a systematic understanding of user behavior in such environments, especially head movement, remains largely unexplored due to the absence of dedicated datasets tailored to this format. This lack of data severely limits progress in viewpoint prediction, attention modeling, and video streaming optimization. To address this critical gap, we introduce ViewGauss-the first publicly available dataset that captures full 6DoF head movement during the viewing of 4D Gaussian splatting videos. Our dataset is collected from 35 participants using a high-precision Vive Focus Vision headset in a controlled environment, while they freely watched four reconstructed Gaussian splatting video sequences derived from the HiFi4G dataset. The data are recorded with high temporal resolution using position coordinates and unit quaternions, and organized into structured CSV files with precise timestamps for downstream synchronization and behavioral analysis. To demonstrate the practical value of ViewGauss, we conduct a preliminary viewpoint prediction experiment using the iTransformer model. The results show that head orientation patterns in 4D Gaussian splatting video scenes are not only temporally coherent but also learnable, highlighting the potential of ViewGauss as a benchmark for future behavioral modeling and predictive rendering systems. The dataset is publicly available at: https://github.com/Cedarleigh/ViewGauss-DataSet.