An Inpainting-Infused Pipeline for Attire and Background Replacement
Felipe Rodrigues Perche-Mahlow André Felipe-Zanella William Alberto Cruz-Castañeda Marcellus Amadeus
In recent years, groundbreaking advancements in Generative Artificial
Intelligence (GenAI) have triggered a transformative paradigm shift,
significantly influencing various domains. In this work, we specifically
explore an integrated approach, leveraging advanced techniques in GenAI and
computer vision emphasizing image manipulation. The methodology unfolds through
several stages, including depth estimation, the creation of inpaint masks based
on depth information, the generation and replacement of backgrounds utilizing
Stable Diffusion in conjunction with Latent Consistency Models (LCMs), and the
subsequent replacement of clothes and application of aesthetic changes through
an inpainting pipeline. Experiments conducted in this study underscore the
methodology's efficacy, highlighting its potential to produce visually
captivating content. The convergence of these advanced techniques allows users
to input photographs of individuals and manipulate them to modify clothing and
background based on specific prompts without manually input inpainting masks,
effectively placing the subjects within the vast landscape of creative
imagination.