Pose and body shape editing in a human image has received increasing attention. However, current methods often struggle with dataset biases and deteriorate realism and the person's identity when users make large edits. We propose a one-shot approach that enables large edits with identity preservation. To enable large edits, we fit a 3D body model, project the input image onto the 3D model, and change the body's pose and shape. Because this initial textured body model has artifacts due to occlusion and the inaccurate body shape, the rendered image undergoes a diffusion-based refinement, in which strong noise destroys body structure and identity whereas insufficient noise does not help. We thus propose an iterative refinement with weak noise, applied first for the whole body and then for the face. We further enhance the realism by fine-tuning text embeddings via self-supervised learning. Our quantitative and qualitative evaluations demonstrate that our method outperforms other existing methods across various datasets.

人物图像中的姿势和身体形状编辑一直受到越来越多的关注。然而，当前的方法在处理大幅度编辑时往往受到数据集偏见的困扰，导致逼真度和人物身份的退化。我们提出了一种一次性方法，可以实现大幅度编辑并保留身份信息。为了实现大幅度编辑，我们使用一个3D人体模型进行拟合，将输入图像投影到3D模型上，并改变人体的姿态和形状。由于初始纹理身体模型存在由于遮挡和不准确的身体形状引起的伪影，渲染图像经历了一种基于扩散的细化过程，在这个过程中，强噪声破坏了身体结构和身份，而不充分的噪声并没有起到帮助作用。因此，我们提出了一种迭代细化方法，首先对整个身体进行弱噪声处理，然后对脸部进行细化。通过自监督学习进一步增强了真实感，通过微调文本嵌入。我们的定量和定性评估表明，我们的方法在各种数据集上优于其他现有方法。

基于扩散的人体姿态和形状编辑