  This paper presents Shape of You (SoY), an approach to improve the accuracy
of 3D body shape estimation for vision-based clothing recommendation systems.
While existing methods have successfully estimated 3D poses, there remains a
lack of work in precise shape estimation, particularly for diverse human
bodies. To address this gap, we propose two loss functions that can be readily
integrated into parametric 3D human reconstruction pipelines. Additionally, we
propose a test-time optimization routine that further improves quality. Our
method improves over the recent SHAPY method by 17.7% on the challenging SSP-3D
dataset. We consider our work to be a step towards a more accurate 3D shape
estimation system that works reliably on diverse body types and holds promise
for practical applications in the fashion industry.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning