Efficiently digitizing high-fidelity animatable human avatars from videos is a challenging and active research topic. Recent volume rendering-based neural representations open a new way for human digitization with their friendly usability and photo-realistic reconstruction quality. However, they are inefficient for long optimization times and slow inference speed; their implicit nature results in entangled geometry, materials, and dynamics of humans, which are hard to edit afterward. Such drawbacks prevent their direct applicability to downstream applications, especially the prominent rasterization-based graphic ones. We present EMA, a method that Efficiently learns Meshy neural fields to reconstruct animatable human Avatars. It jointly optimizes explicit triangular canonical mesh, spatial-varying material, and motion dynamics, via inverse rendering in an end-to-end fashion. Each above component is derived from separate neural fields, relaxing the requirement of a template, or rigging. The mesh representation is highly compatible with the efficient rasterization-based renderer, thus our method only takes about an hour of training and can render in real-time. Moreover, only minutes of optimization is enough for plausible reconstruction results. The disentanglement of meshes enables direct downstream applications. Extensive experiments illustrate the very competitive performance and significant speed boost against previous methods. We also showcase applications including novel pose synthesis, material editing, and relighting. The project page: https://xk-huang.github.io/ema/.

翻译：从视频中高效数字化高保真可动画人体化身是一个具有挑战性且活跃的研究课题。近年来，基于体渲染的神经表示方法因其友好的可用性和逼真的重建质量，为人体数字化开辟了新途径。然而，这类方法存在优化时间长、推理速度慢的问题，其隐式特性导致人体的几何、材质和动力学纠缠在一起，后续编辑困难。这些缺点阻碍了它们直接应用于下游任务，尤其是主流的基于光栅化的图形应用。我们提出EMA方法，通过高效学习可网格化的神经场来重建可动画人体化身。该方法以端到端方式通过逆渲染联合优化显式三角规范网格、空间变化材质和运动动力学。上述每个组件均从独立的神经场导出，无需模板或骨骼绑定。网格表示与高效的光栅化渲染器高度兼容，因此我们的方法仅需约一小时的训练即可实现实时渲染。此外，仅需几分钟的优化即可得到合理的重建结果。网格的解耦使得下游应用可直接实现。大量实验表明，与先前方法相比，该方法在性能上极具竞争力，且速度显著提升。我们还展示了包括新姿态合成、材质编辑和重光照在内的应用。项目页面：https://xk-huang.github.io/ema/。