Multimodal image generation and fusion through content-style hybrid disentanglement

• Research highlight 1: We propose a novel cross-task hybrid training methodology for multimodal images, offering a simple yet unified solution that simultaneously addresses both image generation and fusion tasks. • Research highlight 2: Building upon mutual-supervised multimodal image pairs, we innovatively integrate single-modality self-supervision to develop a hybrid-supervised decoupling framework with a dedicated loss function, achieving robust separation of content-style representations. • Research highlight 3: Extensive experiments spanning on four modalities and seven popular datasets demonstrate our method’s consistent superiority and impressive cross-task capability. Ablation studies further reveal that our framework learns generalized representations transferable across different image processing tasks. Multimodal image fusion and cross-modal translation are fundamental yet challenging tasks in computer vision, with their performance directly impacting downstream applications. Existing approaches typically treat these tasks independently, developing specialized models that fail to exploit the intrinsic relationships between different modalities. This limitation not only restricts model generalizability but also hinders further performance improvements. In this paper, we propose a joint optimization framework for image generation and fusion. Specifically, we generalize multimodal image tasks as the fusion and transformation of cross-modal features, and design a hybrid task training strategy. At the data level, we introduce a self-supervised and mutual-supervised hybrid mechanism for content-style feature decoupling, which achieves superior feature separation through stepwise training on intra-modal and cross-modal data. At the model level, we construct a triple-branch decoupling head along with fusion and transformation modules to ensure synchronous and efficient execution of dual tasks. Our method not only breaks through the single task limitation of the model, but also innovatively introduces mixed supervision into multimodal processing. We conduct comprehensive experiments covering four modalities fusion tasks on seven popular datasets. Extensive experimental results demonstrate that our method achieves superior performance on two tasks as compared of the respective state-of-the-art methods, and show impressive cross-task generalization capability.