发表机构
CVSSP, University of Surrey（萨里大学CVSSP）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于参数高效合成块和深度可分离卷积的高效生成器架构，用于3D高斯头部合成，大幅降低计算和存储开销，并支持在边缘设备上通过CPU或浏览器运行。
AI 中文摘要
生成式3D高斯头部化身能够提供高质量、高效的渲染，但合成高斯表示的计算成本仍然很高，限制了其在资源受限和边缘设备上的部署。我们提出了一种用于无条件3D高斯头部合成的高效生成器架构，该架构基于参数高效的合成块和深度可分离卷积，同时保留基于风格的调节。我们的架构在不进行模型压缩或量化的情况下降低了生成器的复杂度。与基线模型相比，我们的方法将FLOPs减少了94%，参数量减少了70%，模型大小减少了81%，同时保持了有竞争力的生成质量。我们进一步展示了使用ONNX Runtime在移动设备上进行实用的CPU推理和基于浏览器的执行，从而无需专用GPU硬件或特定应用软件即可实现3D高斯化身合成。除了常规的图像质量指标外，我们还评估了多视图一致性、训练成本和部署性能。代码、训练好的模型和评估工具将公开发布。
英文摘要
Generative 3D Gaussian head avatars provide high-quality, efficient rendering, but synthesising the Gaussian representation remains computationally expensive, limiting deployment on resource-constrained and edge devices. We introduce an efficient generator architecture for unconditional 3D Gaussian head synthesis, based on a parameter-efficient synthesis block and depth-wise separable convolutions while retaining style-based conditioning. Our architecture reduces generator complexity without requiring model compression or quantisation. Compared with the baseline model, our approach reduces FLOPs by 94%, parameter count by 70%, and model size by 81%, while maintaining competitive generation quality. We further demonstrate practical CPU inference and browser-based execution on mobile devices using ONNX Runtime, enabling 3D Gaussian avatar synthesis without dedicated GPU hardware or application-specific software. In addition to conventional image-quality metrics, we evaluate multi-view consistency, training cost, and deployment performance. Code, trained models, and evaluation tools will be released publicly.
