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The creation of a parameterized stylized character involves careful selectionof numerous parameters, also known as the "avatar vectors" that can beinterpreted by the avatar engine. Existing unsupervised avatar vectorestimation methods that auto-create avatars for users, however, often fail towork because of the domain gap between realistic faces and stylized avatarimages. To this end, we propose SwiftAvatar, a novel avatar auto-creationframework that is evidently superior to previous works. SwiftAvatar introducesdual-domain generators to create pairs of realistic faces and avatar imagesusing shared latent codes. The latent codes can then be bridged with the avatarvectors as pairs, by performing GAN inversion on the avatar images renderedfrom the engine using avatar vectors. Through this way, we are able tosynthesize paired data in high-quality as many as possible, consisting ofavatar vectors and their corresponding realistic faces. We also proposesemantic augmentation to improve the diversity of synthesis. Finally, alight-weight avatar vector estimator is trained on the synthetic pairs toimplement efficient auto-creation. Our experiments demonstrate theeffectiveness and efficiency of SwiftAvatar on two different avatar engines.The superiority and advantageous flexibility of SwiftAvatar are also verifiedin both subjective and objective evaluations.

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