  The fashion e-commerce industry has witnessed significant growth in recent
years, prompting exploring image-based virtual try-on techniques to incorporate
Augmented Reality (AR) experiences into online shopping platforms. However,
existing research has primarily overlooked a crucial aspect - the runtime of
the underlying machine-learning model. While existing methods prioritize
enhancing output quality, they often disregard the execution time, which
restricts their applications on a limited range of devices. To address this
gap, we propose Distilled Mobile Real-time Virtual Try-On (DM-VTON), a novel
virtual try-on framework designed to achieve simplicity and efficiency. Our
approach is based on a knowledge distillation scheme that leverages a strong
Teacher network as supervision to guide a Student network without relying on
human parsing. Notably, we introduce an efficient Mobile Generative Module
within the Student network, significantly reducing the runtime while ensuring
high-quality output. Additionally, we propose Virtual Try-on-guided Pose for
Data Synthesis to address the limited pose variation observed in training
images. Experimental results show that the proposed method can achieve 40
frames per second on a single Nvidia Tesla T4 GPU and only take up 37 MB of
memory while producing almost the same output quality as other state-of-the-art
methods. DM-VTON stands poised to facilitate the advancement of real-time AR
applications, in addition to the generation of lifelike attired human figures
tailored for diverse specialized training tasks.
https://sites.google.com/view/ltnghia/research/DMVTON

Computer Vision and Pattern Recognition