Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All (AI summary)

Key Points

1. "Diffuse to Choose" (DTC) is a novel diffusion-based image-conditioned inpainting model designed for the "Virtual Try-All" (Vit-All) application in online shopping, aiming to integrate e-commerce items into user images while preserving item details.

2. The model efficiently balances fast inference with the retention of high-fidelity details in a given reference item while ensuring accurate semantic manipulations in the given scene content.

3. DTC incorporates fine-grained cues from the reference image directly into the latent feature maps of the main diffusion model, addressing the limitations of traditional image-conditioned diffusion models in capturing fine-grained details of products.

4. It fulfills three primary conditions for the Vit-All use case: efficiently handles in-the-wild images and references, adeptly preserves the fine-grained details of products while ensuring their seamless integration into the scene, and facilitates rapid zero-shot inference.

5. DTC surpasses existing diffusion-based inpainting methods and matches the performance of non-real-time, few-shot personalization models within the Vit-All context.

6. The model utilizes a secondary U-Net encoder to infuse fine-grained signals from the reference image into the primary U-Net decoder using basic affine transformation layers within a latent diffusion model.

7. Evaluation of DTC shows its superiority in preserving the fine-grained details of items compared to existing models, particularly in handling basic items with minimal details and inpainting detailed items.

8. The model has limitations in handling fine-grained details like text engravings and altering human poses, but overall it outperforms existing diffusion-based inpainting approaches in the Vit-All setting.

9. Comparative evaluation against DreamPaint and PBE variants using datasets and human studies indicates that DTC performs on par with few-shot personalization models, despite being a zero-shot model.

Summary

The paper introduces "Diffuse to Choose," a novel diffusion inpainting model designed for the Vit-All application, aiming to enhance the immersive shopping experience by allowing users to virtually place any e-commerce item in any setting. The authors discuss the limitations of existing specialized solutions and the emergence of diffusion models for addressing the need for a more immersive shopping experience. They highlight the limitations of prior approaches such as DreamPaint and Paint By Example (PBE) and propose "Diffuse to Choose" (DTC) as a solution that effectively balances fast inference with high-fidelity details and accurate semantic manipulations.

The functionality and features of DTC are outlined, emphasizing its ability to fulfill all three criteria for the Vit-All use case and its training process, along with performance evaluations. The paper discusses the results of extensive testing on in-house and publicly available datasets, demonstrating the superiority of DTC over existing diffusion inpainting methods while also matching the performance of few-shot personalization models. The authors address the limitations of DTC, such as potential oversight of fine-grained details, particularly in text engravings, and the model's potential alteration of human poses due to the lack of pose consideration.

The authors conclude that "Diffuse to Choose" outperforms existing diffusion-based inpainting approaches, particularly in the Virtual Try-All setting, and discuss the potential of their model for enhancing the immersive shopping experience.

Reference: https://arxiv.org/abs/2401.13795