[2603.19667] Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19667 (cs) [Submitted on 20 Mar 2026] Title:Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding Authors:Zhijian Gong, Tianren Yao, Wenjia Dong, Xueyuan Xu View a PDF of the paper titled Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding, by Zhijian Gong and 3 other authors View PDF HTML (experimental) Abstract:Human visual reconstruction aims to reconstruct fine-grained visual stimuli based on subject-provided descriptions and corresponding neural signals. As a widely adopted modality, Electroencephalography (EEG) captures rich visual cognition information, encompassing complex spatial relationships and chromatic details within scenes. However, current approaches are deeply coupled with an alignment framework that forces EEG features to align with text or image semantic representation. The dependency may condense the rich spatial and chromatic details in EEG that achieved mere conditioned image generation rather than high-fidelity visual reconstruction. To address this limitation, we propose a novel Joint-Modal Visual Reconstruction (JMVR) framework. It treats EEG and text as independent modalities for joint learning to preserve EEG-specific information for reconstruction. It further employs a multi-scale EEG encoding strategy to capture both fine- and coarse-grained features, al...