Semantic Image Analogy with a Conditional Single-Image GAN

Li, Jiacheng; Xiong, Zhiwei; Liu, Dong; Chen, Xuejin; Zha, Zheng-Jun

doi:10.1145/3394171.3413601

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…ConSinGAN [9] extended SinGAN by improving the rescaling for multi-stage training and training several stages concurrently, which enabled reducing the size of the model and making the training more efficient. To explore the semantic meaning of patches inside a single image, [22] used a conditional single image GAN with the segmentation map as dense conditional input.…”

Section: Related Workmentioning

confidence: 99%

One-Shot GAN: Learning to Generate Samples from Single Images and Videos

Sushko

Gall

Khoreva

2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Training video & generated samples from a single video Training image & generated samples from a single image Figure 1: One-Shot GAN images generated from a single video or a single image. Our model successfully operates in different one-shot settings, including learning from a single video (first two rows) or a single image (last three rows), generating new scene compositions with varying content and layout. For example, from the single training video with a car on the road, One-Shot GAN generates images without a car or with two cars; for the single air balloon image, it can synthesize layouts with a different number and position of the balloons in the scene. (Original samples are shown in grey or red frames.

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Section: Related Workmentioning

confidence: 99%