Cross-modal Latent Space Alignment for Image to Avatar Translation
We present a novel method for automatic vectorized avatar generation from a single portrait image. Most existing approaches that create avatars rely on image-to-image translation methods, which present some limitations when applied to 3D rendering, animation, or video. Instead, we leverage modality-specific autoencoders trained on large-scale unpaired portraits and parametric avatars, and then learn a mapping between both modalities via an alignment module trained on a significantly smaller amount of data. The resulting cross-modal latent space preserves facial identity, producing more visually appealing and higher fidelity avatars than previous methods, as supported by our quantitative and qualitative evaluations. Moreover, our method's virtue of being resolution-independent makes it highly versatile and applicable in a wide range of settings.
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