PixelHuman：基于少量图像的可动画神经辐射场
PixelHuman: Animatable Neural Radiance Fields from Few Images
AI总结：
针对现有可动画人体渲染方法依赖大量训练数据、单场景训练耗时长的问题，提出PixelHuman模型，通过规范表示、姿态感知像素对齐特征和数据驱动变形场实现少样本下的可动画人体合成，性能达当前最优。
摘要：
本文提出了PixelHuman，这是一种新型人体渲染模型，能够从身份、视角和姿态均未知的人物少量图像中生成可动画的人体场景。此前的研究在新视角和新姿态合成方面已展现出不错的性能，但它们依赖大量图像进行训练，且需针对每个场景从视频中单独训练，这导致从未知人体图像生成可动画场景需要耗费大量时间。我们的方法与现有方法的不同之处在于，它能够泛化到任意输入图像以进行可动画人体合成。给定随机姿态序列，我们的方法通过以规范表示和姿态感知像素对齐特征为条件的神经辐射场合成每个目标场景，这两者都可以通过以数据驱动方式学习的变形场获得。实验表明，我们的方法在基于少样本图像的多视角和新姿态合成任务上取得了SOTA（当前最优）性能。
英文摘要：
In this paper, we propose PixelHuman, a novel human rendering model that generates animatable human scenes from a few images of a person with unseen identity, views, and poses. Previous work have demonstrated reasonable performance in novel view and pose synthesis, but they rely on a large number of images to train and are trained per scene from videos, which requires significant amount of time to produce animatable scenes from unseen human images. Our method differs from existing methods in that it can generalize to any input image for animatable human synthesis. Given a random pose sequence, our method synthesizes each target scene using a neural radiance field that is conditioned on a canonical representation and pose-aware pixel-aligned features, both of which can be obtained through deformation fields learned in a data-driven manner. Our experiments show that our method achieves state-of-the-art performance in multiview and novel pose synthesis from few-shot images.
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。
