Abstract
Example-guided image synthesis has recently been attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplar image provides the style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the existing models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is a scene image that is semantically different from the given label map. To this end, we first propose a Masked Spatial-Channel Attention (MSCA) module which models the correspondence between two scenes via efficient decoupled attention. Next, we propose an end-to-end network for joint global and local feature alignment and synthesis. Finally, we propose a novel self-supervision task to enable training. Experiments on the large-scale and more diverse COCO-stuff dataset show significant improvements over the existing methods. Moreover, our approach provides interpretability and can be readily extended to other content manipulation tasks including style and spatial interpolation or extrapolation.
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Notes
- 1.
We assume spatial resolution at scale i being \(H\times W\) and channel size of \(F^{(i)}_{x,2}\), \(F^{(i)}_{c,1}\), \(F^{(i)}_{c,2}\) being \(N,M_1,M_2\), respectively.
- 2.
This differs from the typical retrieve task that uses the top-1 image since the top 20 percentile images tend to be more semantically different from the target label maps.
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Acknowledgment
This work is supported in part by NSF awards IIS-1704337, IIS-1722847, and IIS-1813709, as well as our corporate sponsors.
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Zheng, H., Liao, H., Chen, L., Xiong, W., Chen, T., Luo, J. (2020). Example-Guided Image Synthesis Using Masked Spatial-Channel Attention and Self-supervision. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_25
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