Multi-Attribute Guided Painting Generation
This work addresses the need for more precise control in image stylization for applications in art and design, though it is incremental in building on existing style transfer methods.
The paper tackles the problem of controllable painting generation by decoupling intrinsic properties like artist, genre, and period as control conditions, achieving satisfactory performance through qualitative and quantitative results.
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.