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arXiv · 2606.00954

COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation

Xinlong Zhang·Jia Wei·Xiaoyu Zhang·Teng Zhou·Chengyu Lin·Yongchuan Tang
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Abstract

Achieving high-fidelity object-level control in Diffusion Transformers remains a significant challenge despite the introduction of structural priors like depth and Canny maps. Current object-level conditional generation methods frequently suffer from visual artifacts and struggle to maintain precise control over objects within small localized regions. To address these limitations, we propose Cascaded Object-Level Latent Refinement (COLLAR), a training-free framework that progressively optimizes object-level features via the Field-of-View (FoV) expansion. First, we propose the Cross-Scale Semantic Alignment (CSSA) module to address spatial-semantic gaps by injecting object-level features into extended-FoV branches via attention mechanisms. To further optimize these features, the Cyclic Feature Injection (CFI) module introduces a reciprocal background feedback mechanism. It leverages a frequency-based adaptive strategy to selectively update the global backbone with context-aligned local information. Finally, the extended-FoV branch serves as a hub for feature optimization, ensuring that object-level features are integrated into the global generation process without compromising final image quality. Extensive experiments on the COCO-MIG and COCO-POS benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods across semantic alignment, image quality, and spatial fidelity.

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Xinlong Zhang, Jia Wei, Xiaoyu Zhang, Teng Zhou, Chengyu Lin, Yongchuan Tang. 2026-05-31. COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation. https://arxiv.org/abs/2606.00954

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