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

翻译：我们提出SMPLitex方法，用于从单张图像中估计并操控人体的完整3D外观。SMPLitex基于近期提出的2D图像生成模型，通过输入图像上计算的像素与表面对应关系，将其应用扩展到3D领域。为此，我们首先训练一个完整的3D人体外观生成模型，然后通过将该生成模型条件化为被摄对象的可见部分，使其拟合到输入图像中。此外，我们提出了一个高质量人体纹理的新数据集，该数据集通过基于被摄对象描述和图像的SMPLitex条件采样构建。我们在三个公开数据集上进行了定量与定性评估，结果表明SMPLitex在人体纹理估计任务上显著优于现有方法，同时支持编辑、合成与操控等更广泛的任务。