Visual text rendering represents a fundamental capability of large-scale text-to-image (T2I) models, yet achieving precise word-level controllability remains a significant challenge in this domain. While existing approaches primarily focus on text content accuracy, they often fail to provide fine-grained control over typographic attributes at the word level. To address this limitation, we introduce a comprehensive solution comprising three key components: (1) a novel word-level controlled scene text dataset and benchmark, (2) the Text-Image Alignment (TIA) framework that leverages cross-modal correspondence between textual queries and local image regions through grounding models, and (3) WordCon, a hybrid parameter-efficient fine-tuning (PEFT) method that employs selective parameter reparameterization to enhance both computational efficiency and model portability. The proposed framework incorporates additive supervision mechanisms: a masked loss at the latent level to focus on text regions, and a joint-attention loss at the feature level to promote disentanglement between different words. Extensive experimental evaluations demonstrate that our approach outperforms state-of-the-art methods in both qualitative and quantitative metrics. The proposed method exhibits remarkable versatility, enabling seamless integration with diverse pipelines, including artistic text rendering and image-conditioned text generation. Our datasets, source code, and models will be available for academic research.

Visual text rendering are widespread in various real-world applications, requiring careful font selection and typographic choices. Recent progress in diffusion transformer (DiT)-based text-to-image (T2I) models show promise in automating these processes. However, these methods still encounter challenges like inconsistent fonts, style variation, and limited fine-grained control, particularly at the word-level. This paper proposes a two-stage DiT-based pipeline to address these problems by enhancing controllability over typography and style in text rendering. We introduce typography control fine-tuning (TC-FT), an parameter-efficient fine-tuning method (on $5\%$ key parameters) with enclosing typography control tokens (ETC-tokens), which enables precise word-level application of typographic features. To further address style inconsistency in text rendering, we propose a text-agnostic style control adapter (SCA) that prevents content leakage while enhancing style consistency. To implement TC-FT and SCA effectively, we incorporated HTML-render into the data synthesis pipeline and proposed the first word-level controllable dataset. Through comprehensive experiments, we demonstrate the effectiveness of our approach in achieving superior word-level typographic control, font consistency, and style consistency in text rendering tasks. The datasets and models will be available for academic use.

Generative Artificial Intelligence (GenAI) has recently gained immense popularity by offering various applications for generating high-quality and aesthetically pleasing content of image, 3D, and video data format. The innovative GenAI solutions have shifted paradigms across various design-related industries, particularly fashion. In this paper, we explore the incorporation of GenAI into fashion-related tasks and applications. Our examination encompasses a thorough review of more than 470 research papers and an in-depth analysis of over 300 applications, focusing on their contributions to the field. These contributions are identified as 13 tasks within four categories: multi-modal fashion understanding, and fashion synthesis of image, 3D, and dynamic (video and animatable 3D) formats We delve into these methods, recognizing their potential to propel future endeavours toward achieving state-of-the-art (SOTA) performance. Furthermore, we present a comprehensive overview of 53 publicly available datasets suitable for training and benchmarking fashion-centric models, accompanied by the relevant evaluation metrics. Finally, we review real-world applications, unveiling existing challenges and future directions. With comprehensive investigation and in-depth analysis, this paper is targeted to serve as a useful resource for understanding the current landscape of GenAI in fashion, paving the way for future innovations in this dynamic field. Papers discussed in this paper, along with public code and datasets links are available at: https://github.com/wendashi/Cool-GenAI-Fashion-Papers/ .