提出螺旋展开生成图像的新方法，提升效率与质量。
Learning to Expand Images for Efficient Visual Autoregressive Modeling
- 从中心向外围螺旋生成图像令牌，模拟人类视觉感知。
- 支持动态调整每步生成数量，实现高效并行解码。
- 在ImageNet上达到最优的生成质量与速度平衡。
自回归模型通过离散令牌序列在图像生成中展现出巨大潜力，类似语言建模。然而，现有方法常因逐令牌解码或多层次表示复杂性导致效率低下。本文提出扩张自回归表示（EAR），一种模仿人类视觉系统中心向外感知模式的新生成范式。EAR以螺旋顺序从图像中心逐步向外展开令牌，保持空间连续性，并支持高效并行解码。为进一步提升灵活性与速度，提出长度自适应解码策略，动态调整每步预测令牌数。该生物启发设计不仅降低计算开销，还通过生成顺序与感知相关性对齐提升生成质量。在ImageNet上的大量实验表明，EAR在单尺度自回归模型中实现了最先进的保真度与效率权衡，为可扩展且认知一致的自回归图像生成指明新方向。
原文摘要 · Abstract (English)
Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often suffer from inefficiency, either due to token-by-token decoding or the complexity of multi-scale representations. In this work, we introduce Expanding Autoregressive Representation (EAR), a novel generation paradigm that emulates the human visual system's center-outward perception pattern. EAR unfolds image tokens in a spiral order from the center and progressively expands outward, preserving spatial continuity and enabling efficient parallel decoding. To further enhance flexibility and speed, we propose a length-adaptive decoding strategy that dynamically adjusts the number of tokens predicted at each step. This biologically inspired design not only reduces computational cost but also improves generation quality by aligning the generation order with perceptual relevance. Extensive experiments on ImageNet demonstrate that EAR achieves state-of-the-art trade-offs between fidelity and efficiency on single-scale autoregressive models, setting a new direction for scalable and cognitively aligned autoregressive image generation.
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