Transforming Fashion Styles in Human Imagery with Text-Driven Editing

Core Concepts

Our framework leverages a generative human prior and visual guidance to successfully project abstract fashion concepts onto human images, enabling versatile and compute-efficient fashion style editing.

Abstract

The authors propose a framework called Fashion Style Editing (FaSE) for text-driven fashion style editing on human imagery. The key challenges in this task are the modeling complexity of whole human body imagery and the elusiveness of fashion concepts. To address these challenges, the authors explore two directions to visually reinforce the guidance signal: Textual Augmentation: They enrich the text prompt by querying a language model to generate detailed descriptions for the target fashion concept, making the context more illustrative. Editing with Visual Reference: They construct a reference database of fashion images and retrieve relevant references to guide the model, leveraging the W+ space of the generator as a more illustrative feature space compared to CLIP. The authors also investigate the hierarchical latent space structure of StyleGAN-Human and discover that the mid-level group controls garment shape, enabling targeted fashion editing operations. Qualitative and quantitative evaluations demonstrate the effectiveness of the proposed FaSE framework in projecting diverse fashion styles onto human images, outperforming the baseline text-driven editing method.

Stats

The authors use StyleGAN-Human-v2 as the generative prior and construct a reference database of 12 fashion categories with 35 images each.

Quotes

"Our work extended text-driven image editing to fashion style editing by exploring two techniques that visually clarify the guidance signal." "We hope our framework facilitates new and exciting applications in the fashion field."

Key Insights Distilled From

Fashion Style Editing with Generative Human Prior

by Chaerin Kong... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01984.pdf

Deeper Inquiries

How can the proposed FaSE framework be extended to enable interactive fashion editing, where users can iteratively refine the generated results?

To enable interactive fashion editing with iterative refinement using the FaSE framework, a user-friendly interface can be developed that allows users to provide feedback on the generated results. This feedback loop can be integrated into the system to adjust the editing process based on user input. For example, users could have the option to select specific areas of the image for further refinement or provide additional text prompts to guide the editing process. The system can then use this feedback to fine-tune the editing parameters and generate updated results for the users to review. By incorporating real-time interaction and feedback mechanisms, users can actively participate in the editing process and achieve more personalized and satisfactory results.

What are the potential challenges and limitations of using a generative human prior for fashion style editing, and how can they be addressed?

One potential challenge of using a generative human prior for fashion style editing is the complexity of modeling whole human body imagery with natural garments, body proportions, and poses. This complexity can make it challenging to accurately capture and manipulate intricate fashion details in the generated images. Additionally, the elusiveness of fashion concepts and the subjective interpretation of terms like 'street fashion' can pose difficulties in translating textual descriptions into visually appealing edits. To address these challenges, researchers can focus on improving the latent space representation to better capture fashion-specific features and nuances. This may involve refining the hierarchical structure of the latent space to separate different aspects of fashion styles, such as color, texture, and garment type. Additionally, incorporating more comprehensive fashion datasets and training the model on a diverse range of fashion styles can help improve the model's ability to generate realistic and diverse fashion edits. Furthermore, integrating user feedback mechanisms, as mentioned in the previous question, can help refine the editing process and enhance the model's performance in capturing user-defined fashion styles.

Given the rapid progress in text-to-image generation, how can the insights from FaSE be leveraged to develop more advanced fashion-focused text-to-image models?

The insights from FaSE can be leveraged to develop more advanced fashion-focused text-to-image models by incorporating the following strategies: Enhanced Textual Augmentation: Building on FaSE's approach of enriching text prompts with detailed descriptions, future models can leverage advanced language models to generate more specific and illustrative fashion concepts. This can help in providing clearer guidance to the image editing process and improving the fidelity of the generated fashion edits. Incorporating Visual References: Following FaSE's methodology of using visual references for target concepts, future models can create larger and more diverse fashion image databases to provide a broader range of visual guidance. By training the model to align with reference images in the latent space, the model can learn to generate more accurate and realistic fashion edits based on specific style references. Iterative User Feedback: Implementing interactive user feedback mechanisms, as suggested in FaSE for iterative refinement, can be crucial for developing more advanced text-to-image models. By allowing users to actively participate in the editing process and provide real-time feedback, models can adapt and improve based on user preferences, leading to more personalized and satisfactory fashion edits. By incorporating these insights and strategies, researchers can advance the field of fashion-focused text-to-image generation and create more sophisticated models capable of producing high-quality and diverse fashion edits.