Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same person with the same clothing in different poses. However, obtaining sufficiently large datasets with paired data is challenging and costly. Previous methods that forego paired supervision lack realism. We propose a self-supervised framework named SPICE (Self-supervised Person Image CrEation) that closes the image quality gap with supervised methods. The key insight enabling self-supervision is to exploit 3D information about the human body in several ways. First, the 3D body shape must remain unchanged when reposing. Second, representing body pose in 3D enables reasoning about self occlusions. Third, 3D body parts that are visible before and after reposing, should have similar appearance features. Once trained, SPICE takes an image of a person and generates a new image of that person in a new target pose. SPICE achieves state-of-the-art performance on the DeepFashion dataset, improving the FID score from 29.9 to 7.8 compared with previous unsupervised methods, and with performance similar to the state-of-the-art supervised method (6.4). SPICE also generates temporally coherent videos given an input image and a sequence of poses, despite being trained on static images only.

翻译：以单一图像合成新人图像是一个非常模糊的任务。 多数现有方法都需要配对培训图像, 即穿不同姿势的同一人图像。 然而, 获得足够大的数据套与配对数据具有挑战性和成本很高。 先前的放弃对齐监督的方法缺乏现实性。 我们提议一个名为 SPICE( 自我监督的个人图像测试) 的自我监督框架, 以监督的方法缩小图像质量差距。 使自我监督的关键洞察是以多种方式利用关于人体的3D信息。 首先, 3D身体形状在重新投影时必须保持不变。 第二, 3D中代表身体的数据集能够解释自我覆盖。 第三, 3D在重新投影之前和之后可见的3D身体部分应当具有相似的外观特征。 SPICE( 自我监督个人图像仪) 一旦经过培训, 将一个人的形象生成出一个新的目标。 SPICE 能够以多种方式探索3D 。 首先, 3D 3D形形状在重新投影时必须保持不变。 第二, FID 的评分分数从29.9到7.8,,, 和 以先前的连续的投影制图像, 制成为相同的图像, 。