基于神经图像的人体化身：面向人体化身建模的可泛化辐射场
Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling
AI总结：
本文提出NIA方法，通过混合隐式身体NeRF与基于图像的渲染，从稀疏多视角图像中实现任意人体的新视角与新姿态合成，在身份泛化和姿态泛化上均优于现有方法。
摘要：
我们提出了一种方法，能够从稀疏多视角图像中合成任意人类表演者的新视角和新姿态。我们方法的一个关键组成部分是一个混合外观混合模块，它结合了隐式身体 NeRF 表示和基于图像的渲染的优势。现有的以身体模型为条件的可泛化人体 NeRF 方法，在面对任意人类表演者的几何变化时表现出鲁棒性，但当泛化到未见过的身份时，往往会产生模糊的结果。与此同时，基于图像的渲染在观测充分时能呈现高质量结果，但在稀疏视角设置下会出现伪影。我们提出 Neural Image-based Avatars (NIA)，该方法利用这两种方法的优点：在新的关节姿态和自遮挡下保持鲁棒性，同时直接利用可用的（稀疏）源视角颜色来保留新主体身份的外观细节。我们的混合设计在域内身份泛化以及具有挑战性的跨数据集泛化设置中均优于近期方法。此外，在姿态泛化方面，我们的方法甚至优于逐主体优化的可动画化 NeRF 方法。视频结果可在 https://youngjoongunc.github.io/nia 获取。
英文摘要：
We present a method that enables synthesizing novel views and novel poses of arbitrary human performers from sparse multi-view images. A key ingredient of our method is a hybrid appearance blending module that combines the advantages of the implicit body NeRF representation and image-based rendering. Existing generalizable human NeRF methods that are conditioned on the body model have shown robustness against the geometric variation of arbitrary human performers. Yet they often exhibit blurry results when generalized onto unseen identities. Meanwhile, image-based rendering shows high-quality results when sufficient observations are available, whereas it suffers artifacts in sparse-view settings. We propose Neural Image-based Avatars (NIA) that exploits the best of those two methods: to maintain robustness under new articulations and self-occlusions while directly leveraging the available (sparse) source view colors to preserve appearance details of new subject identities. Our hybrid design outperforms recent methods on both in-domain identity generalization as well as challenging cross-dataset generalization settings. Also, in terms of the pose generalization, our method outperforms even the per-subject optimized animatable NeRF methods. The video results are available at https://youngjoongunc.github.io/nia
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。
