Human avatar has become a novel type of 3D asset with various applications. Ideally, a human avatar should be fully customizable to accommodate different settings and environments. In this work, we introduce NECA, an approach capable of learning versatile human representation from monocular or sparse-view videos, enabling granular customization across aspects such as pose, shadow, shape, lighting and texture. The core of our approach is to represent humans in complementary dual spaces and predict disentangled neural fields of geometry, albedo, shadow, as well as an external lighting, from which we are able to derive realistic rendering with high-frequency details via volumetric rendering. Extensive experiments demonstrate the advantage of our method over the state-of-the-art methods in photorealistic rendering, as well as various editing tasks such as novel pose synthesis and relighting. The code is available at https://github.com/iSEE-Laboratory/NECA.

翻译：人体化身已成为一种新型三维资产，具有广泛的应用前景。理想情况下，人体化身应具备完全可定制性以适应不同场景和环境。本文提出NECA方法，该技术能够从单目或稀疏视角视频中学习通用人体表征，实现姿态、阴影、形状、光照和纹理等多维度的精细化定制。其核心在于通过互补的双空间表征人体，并预测几何、反照率、阴影以及外部光照的解耦神经场，进而利用体渲染技术生成具有高频细节的逼真渲染效果。大量实验表明，本方法在照片级渲染以及新姿态合成、重光照等编辑任务中均优于现有最优方法。代码地址：https://github.com/iSEE-Laboratory/NECA。