HQ-RTVF: High-Quality Real-Time Virtual Try-On Fitting for Diverse Clothing and Body Morphologies

The ability to virtually try on clothing items has become an increasingly important feature for e-commerce and online shopping experiences. Real-time virtual try-on remains challenging because existing methods force a trade-off between speed and quality GAN-based approaches achieve high visual fidelity but at low frame rates, while faster methods sacrifice realism. HQ-RTVF is a diffusion-based framework that resolves this trade-off through three architectural innovations: running the diffusion U-Net entirely in the VAE’s compressed latent space (64×64×4 instead of 512×512×3), limiting denoising to 20 steps with FP16 mixed-precision computation, and parallelizing pose estimation and garment encoding to eliminate sequential bottlenecks. The system uses DensePose and DeepLabv3+ for body pose and segmentation, a CLIP-based garment encoder for fine-grained fabric representation, and an attention-guided fusion decoder that maintains temporal coherence across video frames— distinguishing it from static image methods like VITON-HD and HR-VITON. An adaptive masking mechanism handles diverse garment types from cropped tops to full-length dresses. Evaluated on VITON-HD and DressCode datasets, HQ-RTVF achieves SSIM of 0.950 and LPIPS of 0.067, while operating in real-time with only 4.2 GB GPU memory.

@article{KACHBAL2026,
  title     = {HQ-RTVF: High-Quality Real-Time Virtual Try-On Fitting for Diverse Clothing and Body Morphologies},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {2},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ilham KACHBAL and Khadija Arhid and Said El Abdellaoui},
  doi       = {10.14569/IJACSA.2026.0170291},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170291}
}