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 this https URL

NECA 是一种能够从单目或稀疏视点视频中学习多用途的人体表示的方法，能够通过体积渲染从几何、色素、阴影和外部光照等分离的神经场预测来获得具有高频细节的逼真渲染，并在光照方面具有超越最先进方法的优势。

NECA：神经可定制人形化身

NECA: Neural Customizable Human Avatar

We propose NECA, a deep representation learning method for categorical data.
Built upon the foundations of network embedding and deep unsupervised
representation learning, NECA deeply embeds the intrinsic relationship among
attribute values and explicitly expresses data objects with numeric vector
representations. Designed specifically for categorical data, NECA can support
important downstream data mining tasks, such as clustering. Extensive
experimental analysis demonstrated the effectiveness of NECA.

本研究提出了 NECA，一种针对分类数据的深度表示学习方法，它将网络嵌入和深度无监督表示学习基础上，深度嵌入属性值之间的内在关系，并显式地用数值向量表示数据对象。NECA 可支持如聚类等重要的下游数据挖掘任务，并通过广泛的实验分析证明了其有效性。