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TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

CVPR 2021 · Image generation › Editing and style transfer · approach: Identity-preserving face manipulation · 360 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use TediGAN instead of the strongest reported baseline, ControlGANText-to-image generation · Multi-modal CelebA-HQFID: 116.32 -> 106.37Helped
Use TediGAN instead of AttnGANText-to-image generation · Multi-modal CelebA-HQLPIPS: 0.512 -> 0.456Helped
Use TediGAN instead of the strongest reported baseline, DFGANText-to-image generation · Multi-modal CelebA-HQaccuracy (%): 22.8 -> 25.3Helped
Use TediGAN instead of the strongest reported baseline, DFGANText-to-image generation · Multi-modal CelebA-HQrealism (%): 25.5 -> 31.7Helped
Use TediGAN instead of ManiGANText-guided image manipulation · CelebAFID: 117.89 -> 107.25Helped
Use TediGAN instead of ManiGANText-guided image manipulation · CelebAaccuracy (%): 40.9 -> 59.1Helped
Use TediGAN instead of ManiGANText-guided image manipulation · CelebArealism (%): 36.2 -> 63.8Helped
Use TediGAN instead of ManiGANText-guided image manipulation · Non-CelebAFID: 143.39 -> 135.47Helped
Use TediGAN instead of ManiGANText-guided image manipulation · Non-CelebAaccuracy (%): 12.8 -> 87.2Helped
Use TediGAN instead of ManiGANText-guided image manipulation · Non-CelebArealism (%): 21.7 -> 78.3Helped
Add instance-level optimization to the image-encoder inversion (ablation)TediGAN image manipulation · Image manipulation; qualitative identity preservationidentity preservationHelped
Use the visual-linguistic similarity and pairwise ranking strategy for text-image alignment (ablation)TediGAN text-guided synthesis · Near-miss text-image casesattribute alignmentMixed

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