Despite significant progress in generative image synthesis and full-body
generation in particular, state-of-the-art methods are either
context-independent, overly reliant to text prompts, or bound to the curated
training datasets, such as fashion images with monotonous backgrounds. Here,
our goal is to generate people in clothing that is semantically appropriate for
a given scene. To this end, we present ESP, a novel method for context-aware
full-body generation, that enables photo-realistic inpainting of people into
existing "in-the-wild" photographs. ESP is conditioned on a 2D pose and
contextual cues that are extracted from the environment photograph and
integrated into the generation process. Our models are trained on a dataset
containing a set of in-the-wild photographs of people covering a wide range of
different environments. The method is analyzed quantitatively and
qualitatively, and we show that ESP outperforms state-of-the-art on the task of
contextual full-body generation.

通过在给定场景中融入 2D 姿势和上下文提示，提出了一种用于上下文感知全身生成的新方法 ESP，该方法在现有的 “野外” 照片中实现了真实感人物修复，定量和定性分析表明 ESP 在上下文全身生成的任务上优于现有的方法。