论文标题
通过扭曲3D功能使人类放置
Reposing Humans by Warping 3D Features
论文作者
论文摘要
我们解决了将人的形象放在任何所需的小说姿势中的问题。这项条件形象生成的任务需要关于人类的3D结构的推理，包括自我封闭的身体部位。大多数先前的作品要么基于2D表示，要么需要安装并操纵显式的3D身体网格。基于最新的基于深度学习的体积表示的成功，我们建议从人类图像中隐含地学习密集的特征量，该图像通过明确的几何翘曲将自己用于简单而直观的操作。一旦根据所需的姿势更改扭曲潜在特征体积，卷积解码器将量映射到RGB空间。我们对深层时尚和IPER基准的最先进结果表明，密集的人类代表性值得更详细地进行研究。
We address the problem of reposing an image of a human into any desired novel pose. This conditional image-generation task requires reasoning about the 3D structure of the human, including self-occluded body parts. Most prior works are either based on 2D representations or require fitting and manipulating an explicit 3D body mesh. Based on the recent success in deep learning-based volumetric representations, we propose to implicitly learn a dense feature volume from human images, which lends itself to simple and intuitive manipulation through explicit geometric warping. Once the latent feature volume is warped according to the desired pose change, the volume is mapped back to RGB space by a convolutional decoder. Our state-of-the-art results on the DeepFashion and the iPER benchmarks indicate that dense volumetric human representations are worth investigating in more detail.
