  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.
