AI Virtual Try-On.
AI Virtual Try-On explores the hard parts of garment transfer: preserving the person's pose and identity, keeping clothing texture recognizable, and adapting a product image onto body geometry without making the result feel pasted on.
The Challenge
Virtual try-on is difficult because the model must change clothing while preserving the person, pose, garment identity, and visual realism. Early outputs often blur textures, distort body shape, or ignore the product image.
- Texture blurring in early models
- Pose misalignment
- Inconsistent fabric patterns
- Try-on results that look like overlays instead of worn clothing
The Solution
I built a focused image-to-image pipeline that separates garment alignment from visual refinement, with attention to pose stability, texture retention, and believable final compositing.
- High-fidelity texture preservation
- Support for complex human poses
- Video-backed demo of the end-to-end try-on flow
- Reusable computer-vision learning project for diffusion-based product visualization
Technical Depth
Person + Garment Input
Designed around the practical try-on workflow: combine a person image with a target clothing item and produce a synthesized preview.
Garment Alignment
Focuses on adapting the garment to the body pose and silhouette so clothing follows the person instead of floating over the image.
Texture Preservation
Optimizes for keeping visible fabric cues such as patterns, seams, logos, and material details after synthesis.
Pose Preservation
Keeps the user's original stance and body layout stable while changing the clothing region.
Refinement Pass
Uses a second visual refinement stage to reduce artifacts and make the generated clothing blend more naturally with the original photo.
E-Commerce Use Case
Frames the experiment around a real shopping workflow: previewing a product on a person before purchase.
Technology Stack
Highly specialized modern technology stacks utilized to achieve system goals.