NeuralReshaper：使用深度神经网络的单幅图像人体修图
NeuralReshaper: Single-image Human-body Retouching with Deep Neural Networks
AI总结：
针对单幅图像的语义化人体修图问题，提出 NeuralReshaper，结合参数化三维人体拟合、条件 GAN 与自监督训练，实现全自动且前景背景一致的逼真重塑。
摘要：
本文提出 NeuralReshaper，这是一种利用深度生成网络对单幅图像中的人体进行语义重塑的新方法。为获得全局一致的重塑效果，该方法遵循“先拟合后重塑”流程：首先将参数化三维人体模型拟合到源人体图像，然后根据用户指定的语义属性对拟合得到的三维模型进行重塑。以往方法依赖图像扭曲，将三维重塑效果传递到整个图像域，因此常造成前景和背景失真。相比之下，本文采用以源图像和由重塑后三维模型导出的二维扭曲场为条件的生成对抗网络，以获得更逼真的重塑结果。具体而言，我们使用双头类 UNet 生成器分别编码源图像中的前景与背景信息，并通过特征空间扭曲引导信息流从前景分支流向背景分支。此外，为解决不存在成对数据（即同一人体具有不同体型）所导致的数据缺乏问题，我们引入一种新颖的自监督策略来训练网络。不同于以往常需人工修正因人体到图像拟合不当而产生的不良伪影的方法，本文方法是全自动的。在室内和室外数据集上进行的大量实验证明，本方法优于以往方法。
英文摘要：
In this paper, we present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks. To achieve globally coherent reshaping effects, our approach follows a fit-then-reshape pipeline, which first fits a parametric 3D human model to a source human image and then reshapes the fitted 3D model with respect to user-specified semantic attributes. Previous methods rely on image warping to transfer 3D reshaping effects to the entire image domain and thus often cause distortions in both foreground and background. In contrast, we resort to generative adversarial nets conditioned on the source image and a 2D warping field induced by the reshaped 3D model, to achieve more realistic reshaping results. Specifically, we separately encode the foreground and background information in the source image using a two-headed UNet-like generator, and guide the information flow from the foreground branch to the background branch via feature space warping. Furthermore, to deal with the lack-of-data problem that no paired data exist (i.e., the same human bodies in varying shapes), we introduce a novel self-supervised strategy to train our network. Unlike previous methods that often require manual efforts to correct undesirable artifacts caused by incorrect body-to-image fitting, our method is fully automatic. Extensive experiments on both indoor and outdoor datasets demonstrate the superiority of our method over previous approaches.
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。
