SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model

• URL: http://arxiv.org/abs/2608.07948v1
• Date: Sat, 08 Aug 2026 06:23:48 GMT
• Title: SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model
• Abstract summary: VAR has gained widespread popularity due to its next-scale prediction paradigm.<n>Existing diffusion-based enhancement methods fail to adequately address the unique challenge of cross-scale error propagation and accumulation in VAR.<n>We propose Syn VAR, which introduces a spatial-semantic collaborative control strategy to effectively suppress propagation error and improve generation quality.
• Score: 33.02821148496474
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling complex scenes with multiple objects and attributes. Existing diffusion-based enhancement methods fail to adequately address the unique challenge of cross-scale error propagation and accumulation in VAR. To this end, we propose SynVAR, the first training-free enhancement framework specifically tailored for the VAR paradigm, which introduces a spatial-semantic collaborative control strategy to effectively suppress propagation error and improve generation quality. SynVAR comprises three key components: (1) Global guidance to ensure reasonable spatial structure in the early stages, (2) Receptive field constraints to mitigate early-stage semantic confusion, (3) High-frequency compensation to recover fine-grained details. Extensive quantitative and qualitative experiments demonstrate the significant improvements in the ability of SynVAR to enhance the VAR's capability for complex scene modeling.

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