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Research map / Image generation / Editing and style transfer

SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines

AAAI 2023 · Image generation › Editing and style transfer · approach: Texture and sketch synthesis · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use cross-domain paired-data synthesis and a trained estimator instead of image-level matchingSwiftAvatar · TikTok engineLPIPS: 0.3562 -> 0.3110Helped
Use cross-domain paired-data synthesis and a trained estimator instead of image-level matchingSwiftAvatar · Alter engineLPIPS: 0.2825 -> 0.2405Helped
Use SwiftAvatar rather than the strongest speed-matched baselineTikTok engine · TikTok enginehuman preference: 4.37% -> 59.11%Helped
Use SwiftAvatar rather than the strongest speed-matched baselineAlter engine · Alter enginehuman preference: 18.94% -> 63.68%Helped
Add domain adaptation to the rendered-avatar image-vector training baseline (ablation)SwiftAvatar · TikTok engineLPIPS: 0.5033 -> 0.3401Helped
Add domain adaptation to the rendered-avatar image-vector training baseline (ablation)SwiftAvatar · Alter engineLPIPS: 0.3962 -> 0.3123Helped
Add semantic augmentation after domain adaptation (ablation)SwiftAvatar · TikTok engineLPIPS: 0.3401 -> 0.3110Helped
Add semantic augmentation after domain adaptation (ablation)SwiftAvatar · Alter engineLPIPS: 0.3123 -> 0.2405Helped

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