We propose SMPLitex, a method for estimating and manipulating the complete 3D appearance of humans captured from a single image. SMPLitex builds upon the recently proposed generative models for 2D images, and extends their use to the 3D domain through pixel-to-surface correspondences computed on the input image. To this end, we first train a generative model for complete 3D human appearance, and then fit it into the input image by conditioning the generative model to the visible parts of the subject. Furthermore, we propose a new dataset of high-quality human textures built by sampling SMPLitex conditioned on subject descriptions and images. We quantitatively and qualitatively evaluate our method in 3 publicly available datasets, demonstrating that SMPLitex significantly outperforms existing methods for human texture estimation while allowing for a wider variety of tasks such as editing, synthesis, and manipulation

我们提出了SMPLitex，这是一种从单张图片中估计和操作人类完整3D外观的方法。SMPLitex基于最近提出的2D图像生成模型，并通过对输入图像上的像素到表面对应进行计算将其扩展到3D领域。我们首先训练了一个完整的3D人类外观生成模型，然后通过将生成模型与主体的可见部分相关联，将其适应于输入图像。此外，我们提出了一个新的高质量人类纹理数据集，通过对SMPLitex进行主体描述和图像采样构建。我们在三个公开数据集中进行定量和定性评估，结果显示SMPLitex明显优于现有的人类纹理估计方法，同时允许进行更多样化的任务，如编辑，合成和操作。

SMPLitex：基于单个图像的三维人体纹理估计的生成模型和数据集