Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing

Centrala begrepp

Enhancing fashion image editing with multimodal prompts.

Sammanfattning

The article introduces a novel approach, Ti-MGD, for fashion image editing guided by text, human poses, garment sketches, and fabric textures. It extends latent diffusion models to incorporate multiple modalities and demonstrates effectiveness through experimental evaluations on newly annotated datasets.

1. Abstract

   • Fashion illustration importance.
   • Computer vision potential in fashion design.
   • Multimodal-conditioned fashion image editing approach.

2. Introduction

   • Intersection of computer vision and fashion.
   • Lack of attention to text-conditioned fashion image editing.
   • Introduction of multimodal-conditioned fashion image editing task.

3. Proposed Method

   • Human-centric image editing with multimodal prompts.
   • Preliminaries on Stable Diffusion and CLIP.
   • Conditioning on pose maps, sketches, and fabric textures.

4. Collecting Multimodal Fashion Datasets

   • Annotating Dress Code and VITON-HD datasets with textual descriptions, garment sketches, and fabric textures.

5. Experimental Evaluation

   • Implementation details and competitors comparison.
   • Evaluation metrics: FID, KID, PD (Pose Distance), SD (Sketch Distance), TS (Texture Similarity).

Statistik

Our proposed method achieves an FID of 3.46 in the paired setting on Dress Code Multimodal dataset.
The Ti-MGD model shows a KID of 0.66 in the unpaired setting on VITON-HD Multimodal dataset.

Citat

"The proposed approach aims to generate human-centric fashion images guided by multimodal prompts."
"Our contributions include proposing a novel task of multimodal-conditioned fashion image editing."

Viktiga insikter från

Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing

by Alberto Bald... på arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14828.pdf

Djupare frågor

How can the Ti-MGD approach be applied to other domains beyond fashion

The Ti-MGD approach, which leverages multimodal conditioning for fashion image editing, can be applied to various other domains beyond fashion. One potential application is in interior design, where users could input textual descriptions of furniture pieces or room layouts along with sketches and fabric textures to generate realistic images of their design ideas. This approach could streamline the design process for interior decorators and homeowners alike. Additionally, in product design, Ti-MGD could be used to create visual representations of new products based on text descriptions, sketches, and material samples. This would enable designers to quickly iterate on product concepts and visualize them before production.

What are the potential limitations or drawbacks of using multiple modalities for conditioning in image generation

While using multiple modalities for conditioning in image generation offers several advantages such as increased control over the generated output and enhanced realism through diverse inputs, there are also potential limitations and drawbacks to consider. One limitation is the complexity of integrating different types of information seamlessly into the generative model. Balancing the influence of each modality effectively without overwhelming the model or diluting its focus can be challenging. Additionally, collecting annotated data for multiple modalities can be time-consuming and resource-intensive compared to single-modality approaches.
Another drawback is the risk of introducing conflicting signals from different modalities that may lead to inconsistencies or artifacts in the generated images. Ensuring coherence among all conditioning inputs requires careful calibration and validation processes during training. Moreover, incorporating additional modalities may increase computational requirements and training times due to the higher dimensionality of input data.

How might the integration of fabric texture samples impact user customization in generated designs

The integration of fabric texture samples in image generation models like Ti-MGD has a significant impact on user customization in generated designs by providing finer control over visual details. By including fabric textures as a conditioning signal, users can specify not only the style but also intricate details like patterns, materials, and finishes for garments or products being designed virtually.
This level of detail enhances user engagement by allowing them to experiment with different textures realistically before making final decisions on designs. It enables users to tailor their creations more precisely according to their preferences or brand aesthetics while ensuring that virtual prototypes closely resemble real-world outcomes.