Computer vision breakthroughs for virtual fitting
Malette Art's virtual fitting workflow is based on the Diffusion-based TryOnDiffusion architecture, combining human pose estimation and fabric physics simulation algorithms. The implementation consists of a four-layer process: extracting 18 key body nodes from a user's uploaded photo via OpenPose; semantic segmentation of the clothes using DeeplabV3+; applying a physics engine to simulate the draping effect of different fabrics; and finally synthesizing the natural folds and shadows via a latent diffusion model. The process takes only 45 seconds to generate a 1024×768 resolution image with 8GB of graphics memory.
Tests by apparel retailers have shown that the technology has reduced product returns by 33% and increased page dwell time by 2.4 times. The system supports trying on multiple combinations of clothing at the same time, and can intelligently recommend the best matching program. Currently, 1700+ stores have accessed this feature, generating an average of 150,000 fitting pictures per month.
This answer comes from the articleMalette Art: an AI-based authoring platform for workflow-generated images and videosThe
