Neural Clothing Tryer: Customized Virtual Try-On via Semantic Enhancement and Controlling Diffusion Model

2026-01-30

Expert systems with applications

AI Summary

This work proposes Neural Clothing Tryer, a novel framework that addresses the limited flexibility of existing virtual try-on systems in controlling human appearance, pose, and attributes. For the first time, it integrates semantic descriptions and vision-language aligned features into the virtual try-on task. By combining a controllable diffusion model, a semantic enhancement module, and multimodal conditional inputs, the method achieves high-fidelity preservation of garment details alongside fine-grained human editing capabilities. Experimental results demonstrate that the proposed framework significantly outperforms state-of-the-art approaches on public benchmarks, enabling users to customize digital avatars and accurately overlay specified garments, thereby delivering a high-quality, customizable, and multi-attribute virtual try-on experience.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Problem

• Customized Virtual Try-On
• digital avatar
• garment semantics
• virtual fitting
• appearance customization

Innovation

• Customized Virtual Try-On
• Diffusion Model
• Semantic Enhancement
• Visual-Language Alignment
• Pose and Appearance Editing