Personalized generative recommender systems have emerged as a promising solution for fashion recommendation. However, existing methods primarily rely on implicit visual embeddings from historical interactions, which often contain preference-irrelevant information and result in insufficient user behavior modeling. Moreover, these models typically generate only item images, providing limited interpretability. To address these limitations, we propose DualFashion, a Dual-Diffusional Generative Fashion Recommendation Architecture that jointly models image and text modalities for personalized and explainable recommendation. DualFashion adopts a dual-diffusion Transformer with image and text branches, where structured attribute-level captions and visual outfit information are jointly used as conditioning signals to model user behavior. The proposed architecture produces both fashion item images and textual descriptions, ensuring visual compatibility while providing explicit semantic interpretability. Furthermore, we introduce a text-augmented fine-tuning strategy that enhances generation diversity and enables effective cross-modal knowledge transfer without incurring heavy computational costs. Extensive experiments on iFashion and Polyvore-U across Personalized Fill-in-the-Blank and Generative Outfit Recommendation tasks demonstrate that DualFashion achieves strong performance in behavior modeling, interpretability, and efficiency compared to state-of-the-art methods. Our code and model checkpoints are available at https://github.com/LinkMingzhe/DualFashion.

Personalized outfit generation aims to construct a set of compatible and personalized fashion items as an outfit. Recently, generative AI models have received widespread attention, as they can generate fashion items for users to complete an incomplete outfit or create a complete outfit. However, they have limitations in terms of lacking diversity and relying on the supervised learning paradigm. Recognizing this gap, we propose a novel framework FashionDPO, which fine-tunes the fashion outfit generation model using direct preference optimization. This framework aims to provide a general fine-tuning approach to fashion generative models, refining a pre-trained fashion outfit generation model using automatically generated feedback, without the need to design a task-specific reward function. To make sure that the feedback is comprehensive and objective, we design a multi-expert feedback generation module which covers three evaluation perspectives, i.e., quality, compatibility and personalization. Experiments on two established datasets, i.e., iFashion and Polyvore-U, demonstrate the effectiveness of our framework in enhancing the model's ability to align with users' personalized preferences while adhering to fashion compatibility principles. Our code and model checkpoints are available at https://github.com/Yzcreator/FashionDPO.

The development of virtual try-on has revolutionized online shopping by allowing customers to visualize themselves in various fashion items, thus extending the in-store try-on experience to the cyber space. Although virtual try-on has attracted considerable research initiatives, existing systems only focus on the quality of image generation, overlooking whether the fashion item is a good match to the given person and clothes. Recognizing this gap, we propose to design a one-stop Smart Fitting Room, with the novel formulation of matching-aware virtual try-on. Following this formulation, we design a Hybrid Matching-aware Virtual Try-On Framework (HMaVTON), which combines retrieval-based and generative methods to foster a more personalized virtual try-on experience. This framework integrates a hybrid mix-and-match module and an enhanced virtual try-on module. The former can recommend fashion items available on the platform to boost sales and generate clothes that meets the diverse tastes of consumers. The latter provides high-quality try-on effects, delivering a one-stop shopping service. To validate the effectiveness of our approach, we enlist the expertise of fashion designers for a professional evaluation, assessing the rationality and diversity of the clothes combinations and conducting an evaluation matrix analysis. Our method significantly enhances the practicality of virtual try-on. The code is available at https://github.com/Yzcreator/HMaVTON.

In this paper, we focus on generating photo-realistic images from given text descriptions. Current methods first generate an initial image and then progressively refine it to a high-resolution one. These methods typically indiscriminately refine all granularity features output from the previous stage. However, the ability to express different granularity features in each stage is not consistent, and it is difficult to express precise semantics by further refining the features with poor quality generated in the previous stage. Current methods cannot refine different granularity features independently, resulting in that it is challenging to clearly express all factors of semantics in generated image, and some features even become worse. To address this issue, we propose a Hierarchical Disentangled Representations Generative Adversarial Networks (HDR-GAN) to generate photo-realistic images by explicitly disentangling and individually modeling the factors of semantics in the image. HDR-GAN introduces a novel component called multi-granularity feature disentangled encoder to represent image information comprehensively through explicitly disentangling multi-granularity features including pose, shape and texture. Moreover, we develop a novel Multi-granularity Feature Refinement (MFR) containing a Coarse-grained Feature Refinement (CFR) model and a Fine-grained Feature Refinement (FFR) model. CFR utilizes coarse-grained disentangled representations (e.g., pose and shape) to clarify category information, while FFR employs fine-grained disentangled representations (e.g., texture) to reflect instance-level details. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and CLEVR-SV) demonstrate the rationality and superiority of our method.

Traditional Chinese painting is a unique form of artistic expression. Compared with western art painting, it pays more attention to the verve in visual effect, especially ink painting, which makes good use of lines and pays little attention to information such as texture. Some style transfer methods have recently begun to apply traditional Chinese painting style (such as ink wash style) to photorealistic. Ink stylization of different types of real-world photos in a dataset using these style transfer methods has some limitations. When the input images are animal types that have not been seen in the training set, the generated results retain some semantic features of the data in the training set, resulting in distortion. Therefore, in this paper, we attempt to separate the feature representations for styles and contents and propose a style-woven attention network to achieve zero-shot ink wash painting style transfer. Our model learns to disentangle the data representations in an unsupervised fashion and capture the semantic correlations of content and style. In addition, an ink style loss is added to improve the learning ability of the style encoder. In order to verify the ability of ink wash stylization, we augmented the publicly available dataset $ChipPhi$. Extensive experiments based on a wide validation set prove that our method achieves state-of-the-art results.

The automatic generation of Chinese fonts is challenging due to the large quantity and complex structure of Chinese characters. When there are insufficient reference samples for the target font, existing deep learning-based methods cannot avoid overfitting caused by too few samples, resulting in blurred glyphs and incomplete strokes. To address these problems, this paper proposes a novel deep meta-learning-based font generation method (MLFont) for few-shot Chinese font generation, which leverages existing fonts to improve the generalization capability of the model for new fonts. Existing deep meta-learning methods mainly focus on few-shot image classification. To apply meta-learning to font generation, we present a meta-training strategy based on Model-Agnostic Meta-Learning (MAML) and a task organization method for font generation. The meta-training makes the font generator easy to fine-tune for new font generation tasks. Through random font generation tasks and extraction of glyph content and style separately, the font generator learns the prior knowledge of character structure in the meta-training stage, and then quickly adapts to the generation of new fonts with a few samples by fine-tuning of adversarial training. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods with more complete strokes and less noise in the generated character images.