PrismAvatar：边缘设备上的实时动画3D神经头部 avatar
PrismAvatar: Real-time animated 3D neural head avatars on edge devices
AI总结：
针对边缘设备资源受限问题，提出PrismAvatar模型，通过集成绑定棱柱晶格与3D可变形头部模型，将可变形NeRF提炼为绑定网格和神经纹理，实现边缘设备实时动画渲染且质量与桌面设备最先进模型相当。
摘要：
我们提出PrismAvatar：一种专门设计用于在资源受限的边缘设备上实现实时动画和渲染的3D头部 avatar模型，同时在训练时仍能利用神经体积渲染的优势。通过将绑定的棱柱晶格与3D可变形头部模型（3DMM）集成，我们使用混合渲染模型同时重建基于网格的头部，以及针对3DMM未表示区域的可变形神经辐射场（NeRF）模型。然后，我们将可变形NeRF提炼为绑定网格和神经纹理，这些可以在传统三角形渲染管线的约束下高效地进行动画和渲染。除了在移动设备上以60 fps运行且内存占用低之外，我们发现训练后的模型在桌面设备上的质量与最先进的3D avatar模型相当。
英文摘要：
We present PrismAvatar: a 3D head avatar model which is designed specifically to enable real-time animation and rendering on resource-constrained edge devices, while still enjoying the benefits of neural volumetric rendering at training time. By integrating a rigged prism lattice with a 3D morphable head model, we use a hybrid rendering model to simultaneously reconstruct a mesh-based head and a deformable NeRF model for regions not represented by the 3DMM. We then distill the deformable NeRF into a rigged mesh and neural textures, which can be animated and rendered efficiently within the constraints of the traditional triangle rendering pipeline. In addition to running at 60 fps with low memory usage on mobile devices, we find that our trained models have comparable quality to state-of-the-art 3D avatar models on desktop devices.
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。
