Multi-attribute Guided Painting Generation
2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)(2020)
摘要
Controllable painting generation plays a pivotal role in image stylization. Currently, the control way of style transfer is subject to exemplar-based reference or a random one-hot vector guidance. Few works focus on decoupling the intrinsic properties of painting as control conditions, e.g., artist, genre and period. Under this circumstance, we propose a novel framework adopting multiple attributes from the painting to control the stylized results. An asymmetrical cycle structure is equipped to preserve the fidelity, associating with style preserving and attribute regression loss to keep the unique distinction of colors and textures between domains. Several qualitative and quantitative results demonstrate the effect of the combinations of multiple attributes and achieve satisfactory performance.
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关键词
controllable painting generation,image stylization,style transfer,one-hot vector guidance,asymmetrical cycle structure,style preserving,multiattribute guided painting generation,exemplar-based reference,attribute regression loss
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