发表机构
Monash University（莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出ATA方法，在预训练自回归隐空间中学习视觉属性语义方向，通过简单算术操作实现连续多属性调整，在可控性等指标上优于现有基线。
AI 中文摘要
近期，自回归文本到图像生成已取得显著进展，通过统一生成框架实现了高保真合成。然而，由于属性纠缠以及文本与细粒度视觉表示之间的错位，细粒度语义控制仍具挑战性。本文提出属性标记算术（Attribute Token Arithmetic, ATA），一种能在视觉自回归建模中实现解耦且连续的属性控制的方法。受词嵌入中观察到的向量算术特性启发，ATA直接在预训练的自回归隐空间中识别对应视觉属性（如老化、胖瘦、情绪）的语义方向，这些方向从单张参考图像中学习得到，无需模型重训练或大规模监督。生成过程中，可通过与其他属性标记的简单算术操作，对属性进行连续调整和组合式结合。大量实验表明，ATA能实现保持身份的细粒度多属性调整，在可控性、通用性和计算效率上优于现有自回归编辑基线方法。我们的代码将在该https URL公开。
英文摘要
Autoregressive text-to-image generation has recently achieved remarkable progress, offering high-fidelity synthesis via a unified generative framework. However, fine-grained semantic control remains challenging due to the attribute entanglement and the misalignment between textual and fine-grained visual representations. In this paper, we introduce Attribute Token Arithmetic (ATA), a method that enables disentangled and continuous attribute control in visual autoregressive modelling. Inspired by the vector arithmetic property observed in word embeddings, ATA identifies semantic directions corresponding to visual attributes (e.g., aging, fatness, emotion) directly within the pretrained autoregressive latent space. These directions are learned from a single reference image, without model retraining or large-scale supervision. During generation, attributes can be continuously adjusted and compositionally combined through simple arithmetic operations with other attribute tokens. Extensive experiments demonstrate that ATA achieves identity-preserving, fine-grained, and multi-attribute adjustment, outperforming existing autoregressive editing baselines in controllability, generality, and computational efficiency. Our code will be available at https://github.com/Madaoer/ATA.
