MagicQuill: An Intelligent Interactive Image Editing System
Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Zhiheng Liu, Qifeng Chen, Yujun Shen
2025年份
11顶会引用
摘要
Ant Group, 3 ZJU, 4 HKU Figure 1 . MagicQuill is an intelligent and interactive image editing system built upon diffusion models. Users seamlessly edit images using three intuitive brushstrokes: add, subtract, and color (A). A MLLM dynamically predicts user intentions from their brush strokes and suggests contextual prompts (B1-B4). The examples demonstrate diverse editing operations: to generate a jacket from clothing contour (B1), add a flower crown from head sketches (B2), remove background (B3), and apply color changes to the hair and flowers (B4).
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引用它的顶会 Paper11
- ContextFlow: Training-Free Video Object Editing via Adaptive Context EnrichmentYiyang Chen, Xuanhua He, Xiujun Ma, Jack MaAAAI 2026 · 被引用 17 次
- DesignTrace: Exploring, Iterating and Tracking Design Alternatives with GenAIXiaohan Peng, Debanjana Haldar, Wendy E. Mackay, Janin KochCHI 2026 · 被引用 3 次
- Interaction-Augmented Instruction: Modeling the Synergy of Prompts and Interactions in Human-GenAI CollaborationLeixian Shen, Yifang Wang, Huamin Qu, Xing Xie 等CHI 2026 · 被引用 3 次
- Sketch3DVE: Sketch-based 3D-Aware Scene Video EditingFeng-Lin Liu, Shi-Yang Li, Yan-Pei Cao, Hongbo Fu 等SIGGRAPH 2025 · 被引用 2 次
- Live Interactive Training for Video SegmentationXinyu Yang, Haozheng Yu, Yihong Sun, Bharath Hariharan 等CVPR 2026 · 被引用 1 次
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- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 被引用 11,743 次
- Visual Instruction TuningHaotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae LeeNeurIPS 2023 · 被引用 11,349 次
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