PackUV: 压缩高斯UV映射用于4D体素视频
PackUV: Packed Gaussian UV Maps for 4D Volumetric Video
AI总结：
PackUV通过压缩高斯UV映射实现4D体素视频的高效存储与流式传输，解决了传统方法在长序列和大运动下的时间一致性问题。
摘要：
体素视频提供了沉浸式的4D体验，但仍然难以大规模重建、存储和流式传输。现有的基于高斯点划线的方法虽然能实现高质量的重建，但在长序列中会出现时间不一致的问题，并且在大运动和遮挡情况下会失效。此外，它们的输出通常与传统视频编码流程不兼容，阻碍了实际应用。我们引入了PackUV，一种新的4D高斯表示方法，将所有高斯属性映射到一系列结构化的、多尺度的UV图集，实现了紧凑且图像原生的存储。为了从多视角视频中拟合这种表示，我们提出了PackUV-GS，一种时间一致的拟合方法，直接在UV域中优化高斯参数。一个流引导的高斯标记和视频关键帧模块识别动态高斯，稳定静态区域，并在大运动和遮挡情况下保持时间一致性。所得到的UV图集格式是首个统一的体素视频表示，与标准视频编码器（如FFV1）兼容，而不损失质量，使在现有多媒体基础设施中高效流式传输成为可能。为了评估长时长的体素捕获，我们提出了PackUV-2B，到目前为止最大的多视角视频数据集，拥有超过50台同步相机，显著的运动和频繁的遮挡，覆盖100个序列和2B（十亿）帧。广泛的实验表明，我们的方法在渲染保真度方面优于现有基线，同时能够扩展到长达30分钟的序列，保持一致的质量。
英文摘要：
Volumetric videos offer immersive 4D experiences, but remain difficult to reconstruct, store, and stream at scale. Existing Gaussian Splatting based methods achieve high-quality reconstruction but break down on long sequences, temporal inconsistency, and fail under large motions and disocclusions. Moreover, their outputs are typically incompatible with conventional video coding pipelines, preventing practical applications. We introduce PackUV, a novel 4D Gaussian representation that maps all Gaussian attributes into a sequence of structured, multi-scale UV atlas, enabling compact, image-native storage. To fit this representation from multi-view videos, we propose PackUV-GS, a temporally consistent fitting method that directly optimizes Gaussian parameters in the UV domain. A flow-guided Gaussian labeling and video keyframing module identifies dynamic Gaussians, stabilizes static regions, and preserves temporal coherence even under large motions and disocclusions. The resulting UV atlas format is the first unified volumetric video representation compatible with standard video codecs (e.g., FFV1) without losing quality, enabling efficient streaming within existing multimedia infrastructure. To evaluate long-duration volumetric capture, we present PackUV-2B, the largest multi-view video dataset to date, featuring more than 50 synchronized cameras, substantial motion, and frequent disocclusions across 100 sequences and 2B (billion) frames. Extensive experiments demonstrate that our method surpasses existing baselines in rendering fidelity while scaling to sequences up to 30 minutes with consistent quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
