ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion
  Model
Binghui Chen Wenyu Li Yifeng Geng Xuansong Xie Wangmeng Zuo
With the development of the large-scale diffusion model, Artificial
Intelligence Generated Content (AIGC) techniques are popular recently. However,
how to truly make it serve our daily lives remains an open question. To this
end, in this paper, we focus on employing AIGC techniques in one filed of
E-commerce marketing, i.e., generating hyper-realistic advertising images for
displaying user-specified shoes by human. Specifically, we propose a
shoe-wearing system, called Shoe-Model, to generate plausible images of human
legs interacting with the given shoes. It consists of three modules: (1) shoe
wearable-area detection module (WD), (2) leg-pose synthesis module (LpS) and
the final (3) shoe-wearing image generation module (SW). Them three are
performed in ordered stages. Compared to baselines, our ShoeModel is shown to
generalize better to different type of shoes and has ability of keeping the
ID-consistency of the given shoes, as well as automatically producing
reasonable interactions with human. Extensive experiments show the
effectiveness of our proposed shoe-wearing system. Figure 1 shows the input and
output examples of our ShoeModel.