Shape of You: Precise 3D shape estimations for diverse body types
This work addresses the lack of precise shape estimation for diverse human bodies, with incremental improvements for practical applications in the fashion industry.
This paper tackles the problem of improving 3D body shape estimation accuracy for diverse body types in vision-based clothing recommendation systems, achieving a 17.7% improvement over the SHAPY method on the SSP-3D dataset.
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.