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Yanfeng Li, Kahou Chan, Yue Sun, Chantong Lam, Tong Tong, Zitong Yu, Keren Fu, Xiaohong Liu, Tao Tan
MoEdit: On Learning Quantity Perception for Multi-object Image Editing
https://arxiv.org/abs/2503.10112

results demonstrate that our MoEdit achieves State-Of-The-Art (SOTA) performance in multi-object image editing. Data and codes will be available at https://github.com/Tear-kitty/MoEdit. [7/7 of https://arxiv.org/abs/2503.10112v1]

preserves quantity consistency by effective control in editing, without relying on auxiliary tools. By leveraging the SD model, MoEdit enables customized preservation and modification of specific concepts in inputs with high quality. Experimental [6/7 of https://arxiv.org/abs/2503.10112v1]

both of which are crucial for ensuring consistent quantity perception, resulting in suboptimal perceptual performance. To address these challenges, we propose MoEdit, an auxiliary-free multi-object image editing framework. MoEdit facilitates [3/7 of https://arxiv.org/abs/2503.10112v1]

Yanfeng Li, Kahou Chan, Yue Sun, Chantong Lam, Tong Tong, Zitong Yu, Keren Fu, Xiaohong Liu, Tao Tan: MoEdit: On Learning Quantity Perception for Multi-object Image Editing https://arxiv.org/abs/2503.10112 https://arxiv.org/pdf/2503.10112 https://arxiv.org/html/2503.10112

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