Abstract: The ability to generate realistic and coherent fashion images from textual descriptions and reference photographs remains a significant challenge in computer vision. Existing generative adversarial networks (GANs), while showing promise in image synthesis, often struggle to maintain global structural coherence when conditioned on both visual and textual inputs. This paper introduces a novel approach to address this limitation, enabling the creation of highly realistic and structurally consistent fashion images ("Be Your Own Prada") that faithfully reflect both a given photograph and a corresponding textual description. We present a multi-stage architecture that leverages both global and local feature representations, ensuring that the generated image adheres to the structural constraints imposed by the input photograph while incorporating the stylistic and detailed information specified in the textual description. Our method significantly improves upon existing state-of-the-art techniques, as demonstrated by quantitative and qualitative evaluations on a benchmark fashion dataset. This work was presented at ICCV17.
1. Introduction:
The field of fashion synthesis has seen considerable advancements in recent years, driven by the increasing power of deep learning techniques, particularly generative adversarial networks (GANs). These models have demonstrated remarkable capabilities in generating realistic images from various types of input, including text descriptions, sketches, and even random noise. However, the task of synthesizing fashion images that are both visually appealing and structurally consistent, given both a reference photograph and a textual description, remains a significant challenge. Existing GAN-based approaches often fail to capture the global structural coherence of the input photograph, leading to generated images that are visually inconsistent or lack the desired overall shape and layout. For example, a system might correctly generate a dress with the specified color and pattern from a textual description, but fail to maintain the pose and overall silhouette presented in the reference photograph. This disconnect between the generated image and the input photograph significantly impacts the realism and usability of the synthesized fashion items.
This paper presents "Be Your Own Prada," a novel approach to fashion synthesis that specifically addresses the problem of global structural coherence. Our method builds upon previous work in GANs and image generation, but incorporates several key innovations to ensure that the generated images faithfully reflect both the visual and textual inputs. We achieve this through a multi-stage architecture that leverages both global and local feature representations, allowing for precise control over the overall structure while simultaneously incorporating detailed stylistic information from the text description. Our approach significantly improves upon the state-of-the-art in terms of structural coherence and visual realism, as demonstrated by quantitative and qualitative evaluations.
2. Related Work:
The field of image synthesis has witnessed significant advancements with the advent of GANs. Early works focused primarily on generating images from random noise or simple sketches [cite relevant papers]. Later research explored the use of conditional GANs (cGANs) to generate images conditioned on textual descriptions [cite relevant papers], enabling a degree of control over the generated content. However, these methods often struggled to maintain consistency when conditioned on multiple inputs, particularly when dealing with complex structures like those found in fashion images.
Several approaches have attempted to incorporate visual information as a condition for image generation [cite relevant papers]. These methods typically use image features as an additional input to the GAN, aiming to guide the generation process towards a visually similar output. However, many of these methods fail to effectively integrate the visual information, leading to a lack of global structural coherence. The challenge lies in balancing the influence of the visual and textual inputs, ensuring that the generated image respects both the overall structure of the reference photograph and the specific details described in the text.
3. Be Your Own Prada: Architecture and Methodology
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