Decomposing Subject-Driven Image Generation via Intermediate Structural Prediction
Subject-driven text-to-image generation still struggles to preserve high-frequency identity details such as logos, patterns, and text. Existing methods typically operate directly in RGB space, which often leads to detail degradation under substantial edits. We propose a two-stage framework that decouples structure from appearance by first predicting a Canny map and then rendering the final image conditioned on both the source appearance and the predicted structure. To improve text handling, we further introduce a fully automatic pipeline that constructs a 100k-pair text-aware dataset with cross-view textual consistency. Experiments, including GPT-4.1-based evaluation and a knowledge disti...
| Authors | Hanzhong Guo, Yizhou Yu |
|---|---|
| Primary category | cs.CV |
| Published | 20 May 2026 |
| Last updated at source | 17 July 2026 |
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