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MISC: Multi-Condition Injection and Spatially-Adaptive Compositing for Conditional Person Image Synthesis
AbstractIn this paper, we explore synthesizing person images with multiple conditions for various backgrounds. To this end, we propose a framework named "MISC" for conditional image generation and image compositing. For conditional image generation, we improve the existing condition injection mechanisms by leveraging the inter-condition correlations. For the image compositing, we theoretically prove the weaknesses of the cutting-edge methods, and make it more robust by removing the spatially-invariance constraint, and enabling the bounding mechanism and the spatial adaptability. We show the effectiveness of our method on the Video Instance-level Parsing dataset, and demonstrate the robustness through controllability tests.
Related Material
[pdf][bibtex]@InProceedings{Weng_2020_CVPR,
author = {Weng, Shuchen and Li, Wenbo and Li, Dawei and Jin, Hongxia and Shi, Boxin},
title = {MISC: Multi-Condition Injection and Spatially-Adaptive Compositing for Conditional Person Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
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