  Virtual try-on, a rapidly evolving field in computer vision, is transforming
e-commerce by improving customer experiences through precise garment warping
and seamless integration onto the human body. While existing methods such as
TPS and flow address the garment warping but overlook the finer contextual
details. In this paper, we introduce a novel graph based warping technique
which emphasizes the value of context in garment flow. Our graph based warping
module generates warped garment as well as a coarse person image, which is
utilised by a simple refinement network to give a coarse virtual tryon image.
The proposed work exploits latent diffusion model to generate the final tryon,
treating garment transfer as an inpainting task. The diffusion model is
conditioned with decoupled cross attention based inversion of visual and
textual information. We introduce an occlusion aware warping constraint that
generates dense warped garment, without any holes and occlusion. Our method,
validated on VITON-HD and Dresscode datasets, showcases substantial
state-of-the-art qualitative and quantitative results showing considerable
improvement in garment warping, texture preservation, and overall realism.
