AI 中文总结
本文提出SACHA框架，通过语义感知密度控制与外观-运动分解压缩，实现动态3D高斯头部化身的紧凑表示与高效传输，且率失真性能优于现有方法，渲染质量高。
AI 中文摘要
可动画化的3D高斯头部化身能提供高保真且灵活的面部渲染效果，但通常需要为大量高斯基元消耗大量存储与传输成本。现有的高斯头部化身方法既未考虑不同头部语义区域的视觉显著性以实现更合理的高斯基元分配，也未对训练后的头部化身序列进行高效压缩。为解决该问题，本文提出SACHA，这是一种动态头部化身压缩框架，它利用语义感知密度控制与外观-运动分解技术，实现头部化身序列的紧凑表示与高质量新视图渲染。具体而言，语义感知密度控制通过区域自适应的密集化与剪枝，引导高斯基元在不同头部区域的自适应分配；此外，外观-运动分解压缩仅传输头部先验参数用于化身运动，进一步降低了化身序列的时间冗余。这些设计共同实现了动态高斯头部化身的紧凑表示与高效传输，同时保留了视觉保真度。实验表明，SACHA在现有高斯头部化身表示与压缩方法中实现了更优的率失真性能，同时保持了高质量的新视图与新表情渲染。
英文摘要
Animatable 3D Gaussian head avatars offer high-fidelity and flexible facial rendering, but typically require substantial storage and transmission costs for numerous Gaussian primitives. Existing Gaussian head avatar methods overlook the visual saliency of different head semantic regions for more appropriate Gaussian primitive allocation, as well as the efficient compression of trained head avatar sequences. To tackle this obstacle, we propose SACHA, a dynamic head avatar compression framework that leverages both semantic-aware density control and appearance-motion decomposition to achieve compact representation and high-quality novel-view rendering of head avatar sequences. Specifically, the semantic-aware density control guides the adaptive allocation of Gaussian primitives across different head regions with region-adaptive densification and pruning. In addition, the appearance-motion decomposed compression further reduces the temporal redundancy of the avatar sequence by transmitting only head-prior parameters for avatar movements. Together, these designs enable a compact representation for efficient transmission of dynamic Gaussian head avatars while preserving visual fidelity. Experiments demonstrate that SACHA achieves a superior rate-distortion performance over existing Gaussian head avatar representation and compression methods while maintaining high-quality novel-view and novel-expression rendering.
