Enhancing Fitness Visualization: Application and Efficacy of Realistic Inpainting Techniques Using Diffusion Models

Abstract

In this paper we present a stable-diffusion based zero-shot approach to realistically transform the image of a

human body into a more fit version of that depicted person. Therefore we combine a modified stable diffusion

model with inpainting techniques and incorporated constraints. We introduce a prototype which allows users to

upload a photo and visualize a more fit version of themselves. We evaluated our approach in various experiments

and focused on the applicability and effectiveness of these techniques, with attention to gender-specific results.

This work contributes to the fields of computer vision and generative AI by demonstrating practical applications

and identifying areas for improvement in realistic body transformation visualizations. (This work is a part of the project (M4-SKI) has been supported and funded by the European Regional Development Fund (ERDF)).

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