用锚点约束高斯分布，实现长时序动态场景的稳定视频重建
ATGS: Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation
- 以时间锚点组织高斯分布，降低长期运动建模复杂度
- 通过时间窗口激活相关锚点，提升长序列渲染效率与连贯性
- 多层级锚点特征联合约束，保障时空稳定性，适合复杂动态场景
体视频可实现真实世界动态场景的沉浸式自由视角渲染，但现有方法在处理长序列和复杂运动时易出现时间不稳和视觉伪影。为此，我们提出基于高斯点阵的体视频重建框架ATGS。核心思想是：直接用独立高斯基元追踪长期复杂运动本质上不稳定。因此，我们围绕时间条件锚点组织高斯分布，使其空间与时间支持被锚点定位，从而降低长程运动复杂度。进一步引入时间窗口策略，仅激活与查询时间相关的锚点，提升可扩展性与时间连贯性。为确保时空稳定性，设计了一组紧凑的多层级锚点特征，编码全局、局部空间及局部时间特征，共同约束高斯生成。大量实验表明，ATGS在复杂运动的长序列体视频上持续优于现有方法。
原文摘要 · Abstract (English)
Volumetric video enables immersive free viewpoint rendering of dynamic real world scenes, yet existing methods struggle with long sequences and complex motions, often leading to temporal instability and visual artifacts. To address these challenges, we propose \ourname, a Gaussian splatting based framework for volumetric video reconstruction. Our key insight is that explicitly tracking long term complex motion with individual Gaussian primitives is inherently unstable. Instead, we organize Gaussians around time conditioned anchors that localize their spatial and temporal support, thereby reducing long range motion complexity. We further introduce a temporal windowing strategy to activate only anchors relevant to the queried time, which improves scalability and temporal coherence. In addition, to ensure spatial and temporal stability, we design a compact set of multi level anchor features that encode global features, local spatial features, and local temporal features, jointly constraining Gaussian generation. Extensive experiments demonstrate that \ourname \ consistently outperforms prior methods on long sequence volumetric videos with complex motions. Project page: https://github.com/WuJH2001/ATGS.
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