DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
Journal:
arXiv
Published Date:
Dec 19, 2024
Abstract
We introduce DiffusionTrend for virtual fashion try-on, which forgoes the
need for retraining diffusion models. Using advanced diffusion models,
DiffusionTrend harnesses latent information rich in prior information to
capture the nuances of garment details. Throughout the diffusion denoising
process, these details are seamlessly integrated into the model image
generation, expertly directed by a precise garment mask crafted by a
lightweight and compact CNN. Although our DiffusionTrend model initially
demonstrates suboptimal metric performance, our exploratory approach offers
some important advantages: (1) It circumvents resource-intensive retraining of
diffusion models on large datasets. (2) It eliminates the necessity for various
complex and user-unfriendly model inputs. (3) It delivers a visually compelling
try-on experience, underscoring the potential of training-free diffusion model.
This initial foray into the application of untrained diffusion models in
virtual try-on technology potentially paves the way for further exploration and
refinement in this industrially and academically valuable field.
