LostGANs generate images from bounding-box layouts with per-object style control, using weakly supervised masks and instance-specific normalization, and report state-of-the-art scores on COCO-Stuff and Visual Genome.
Object-driven Text-to-Image Synthesis via Adversarial Training
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abstract
In this paper, we propose Object-driven Attentive Generative Adversarial Newtorks (Obj-GANs) that allow object-centered text-to-image synthesis for complex scenes. Following the two-step (layout-image) generation process, a novel object-driven attentive image generator is proposed to synthesize salient objects by paying attention to the most relevant words in the text description and the pre-generated semantic layout. In addition, a new Fast R-CNN based object-wise discriminator is proposed to provide rich object-wise discrimination signals on whether the synthesized object matches the text description and the pre-generated layout. The proposed Obj-GAN significantly outperforms the previous state of the art in various metrics on the large-scale COCO benchmark, increasing the Inception score by 27% and decreasing the FID score by 11%. A thorough comparison between the traditional grid attention and the new object-driven attention is provided through analyzing their mechanisms and visualizing their attention layers, showing insights of how the proposed model generates complex scenes in high quality.
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cs.CV 1years
2019 1verdicts
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Image Synthesis From Reconfigurable Layout and Style
LostGANs generate images from bounding-box layouts with per-object style control, using weakly supervised masks and instance-specific normalization, and report state-of-the-art scores on COCO-Stuff and Visual Genome.