Publications

Facial age estimation has shown notable progress under controlled conditions. However, in unconstrained real-world environments, accurate age estimation remains challenging. This difficulty becomes more severe when facial images contain partial occlusions, as these obstructions hide important age-related information. Moreover, there is no publicly available occluded age estimation dataset to improve performance in real-world scenarios. To overcome this issue, we propose and publicly release three new datasets, FG-NET-O8, APPA-REAL-O8, and MORPH-O8, derived from existing benchmarks. These datasets contain eight types of realistic occlusions, providing a comprehensive testbed for age estimation under occlusions. These occlusions are generated using multiple diffusion-based methods, including Stable Diffusion Realistic Vision, Blended Latent Diffusion, and Fooocus, while preserving the facial identity of each subject. We also design and conduct a human survey to evaluate the quality of the generated occlusions. Furthermore, we test five state-of-the-art age estimation approaches to analyze the impact of real-world occlusions on age estimation performance. Experimental results demonstrate that all approaches exhibit severe performance degradation for nearly all occlusion types across all three datasets.

In recent years, the fashion industry has increasingly adopted AI technologies to enhance customer experience, driven by the proliferation of e-commerce platforms and virtual applications. Among the various tasks, virtual try-on and multimodal fashion image editing – which utilizes diverse input modalities such as text, garment sketches, and body poses – have become a key area of research. Diffusion models have emerged as a leading approach for such generative tasks, offering superior image quality and diversity. However, most existing virtual try-on methods rely on having a specific garment input, which is often impractical in real-world scenarios where users may only provide textual specifications. To address this limitation, in this work we introduce Fashion Retrieval-Augmented Generation (Fashion-RAG), a novel method that enables the customization of fashion items based on user preferences provided in textual form. Our approach retrieves multiple garments that match the input specifications and generates a personalized image by incorporating attributes from the retrieved items. To achieve this, we employ textual inversion techniques, where retrieved garment images are projected into the textual embedding space of the Stable Diffusion text encoder, allowing seamless integration of retrieved elements into the generative process. Experimental results on the Dress Code dataset demonstrate that Fashion-RAG outperforms existing methods both qualitatively and quantitatively, effectively capturing fine-grained visual details from retrieved garments. To the best of our knowledge, this is the first work to introduce a retrieval-augmented generation approach specifically tailored for multimodal fashion image editing.

Diffusion models have become the State-of-the-Art for text-to-image generation, and increasing research effort has been dedicated to adapting the inference process of pretrained diffusion models to achieve zero-shot capabilities. An example is the generation of panorama images, which has been tackled in recent works by combining independent diffusion paths over overlapping latent features, which is referred to as joint diffusion, obtaining perceptually aligned panoramas. However, these methods often yield semantically incoherent outputs and trade-off diversity for uniformity. To overcome this limitation, we propose the Merge-Attend-Diffuse operator, which can be plugged into different types of pretrained diffusion models used in a joint diffusion setting to improve the perceptual and semantical coherence of the generated panorama images. Specifically, we merge the diffusion paths, reprogramming self- and cross-attention to operate on the aggregated latent space. Extensive quantitative and qualitative experimental analysis, together with a user study, demonstrate that our method maintains compatibility with the input prompt and visual quality of the generated images while increasing their semantic coherence. We release the code at https://github.com/aimagelab/MAD.