[2603.00589] AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00589 (cs) [Submitted on 28 Feb 2026] Title:AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution Authors:Cencen Liu (1), Dongyang Zhang (1 and 2), Wen Yin (1), Jielei Wang (1 and 2), Tianyu Li (1), Ji Guo (1), Wenbo Jiang (1), Guoqing Wang (1), Guoming Lu (1 and 2) ((1) University of Electronic Science and Technology of China, (2) Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province) View a PDF of the paper titled AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution, by Cencen Liu (1) and 8 other authors View PDF HTML (experimental) Abstract:Visual autoregressive (VAR) models have recently emerged as a promising alternative for image generation, offering stable training, non-iterative inference, and high-fidelity synthesis through next-scale prediction. This encourages the exploration of VAR for image super-resolution (ISR), yet its application remains underexplored and faces two critical challenges: locality-biased attention, which fragments spatial structures, and residual-only supervision, which accumulates errors across scales, severely compromises global consistency of reconstructed images. To address these issues, we propose AlignVAR, a globally consistent visual autoregressive framework tailored for ISR, featuring two key components: (1) Spatial Consistency Autoregression (SCA), which applies an adaptive...