Diffusion models have yielded remarkable success in virtual try-on (VTON) task, yet they often fall short of fully meeting user expectations regarding visual quality and detail preservation. To alleviate this issue, we curate a dataset of synthesized VTON images annotated with human judgments across multiple perceptual criteria. A vision large language model (VLLM), namely VTON-VLLM, is then learnt on these annotations. VTON-VLLM functions as a unified "fashion expert" and is capable of both evaluating and steering VTON synthesis towards human preferences.

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.

Conditional image synthesis aims tocreate an image according tothe given control signals.

Notably, our LLMScore achieves Kendall's