Fine-Grained Controllable Apparel Showcase Image Generation via Garment-Centric Outpainting
This work addresses the need for efficient and detailed fashion image generation for applications in e-commerce and design, though it is incremental as it builds on existing latent diffusion models.
The paper tackles the problem of generating fine-grained controllable apparel showcase images by proposing a garment-centric outpainting framework based on latent diffusion models, which customizes fashion models wearing given garments using text prompts and facial images, achieving superior performance compared to state-of-the-art methods as validated in experiments.
In this paper, we propose a novel garment-centric outpainting (GCO) framework based on the latent diffusion model (LDM) for fine-grained controllable apparel showcase image generation. The proposed framework aims at customizing a fashion model wearing a given garment via text prompts and facial images. Different from existing methods, our framework takes a garment image segmented from a dressed mannequin or a person as the input, eliminating the need for learning cloth deformation and ensuring faithful preservation of garment details. The proposed framework consists of two stages. In the first stage, we introduce a garment-adaptive pose prediction model that generates diverse poses given the garment. Then, in the next stage, we generate apparel showcase images, conditioned on the garment and the predicted poses, along with specified text prompts and facial images. Notably, a multi-scale appearance customization module (MS-ACM) is designed to allow both overall and fine-grained text-based control over the generated model's appearance. Moreover, we leverage a lightweight feature fusion operation without introducing any extra encoders or modules to integrate multiple conditions, which is more efficient. Extensive experiments validate the superior performance of our framework compared to state-of-the-art methods.