Posted by Haolin Jia, Software program Engineer, and Qifei Wang, Senior Software program Engineer, Core ML
Lately, there was a rising curiosity in built-in augmented actuality (AR) experiences utilizing real-time face characteristic era and modifying features in cellular purposes. This consists of purposes briefly movies, digital actuality, and gaming. In consequence, there’s a want for light-weight and high-quality face era and modifying fashions, usually primarily based on generative adversarial community (GAN) strategies. Nevertheless, most GAN fashions are computationally complicated and require a big coaching dataset. Moreover, it is very important use GAN fashions responsibly.
On this submit, we introduce MediaPipe FaceStylizer, an environment friendly design for few-shot face stylization that addresses the challenges of mannequin complexity and information effectivity whereas adhering to Google’s accountable AI Ideas. The mannequin consists of a face generator and a face encoder used as GAN inversion to map pictures into latent code for the generator. Now we have developed a mobile-friendly synthesis community for the face generator, which generates high-quality pictures from coarse to positive granularities. Now we have additionally designed loss features for the auxiliary heads of the generator to distill the coed generator from the trainer StyleGAN mannequin, leading to a light-weight mannequin with excessive era high quality. The MediaPipe FaceStylizer resolution is obtainable in open supply by way of MediaPipe.
Customers can fine-tune the generator utilizing MediaPipe Mannequin Maker to be taught a method from one or a couple of pictures. They’ll then deploy the personalized mannequin to on-device face stylization purposes utilizing MediaPipe FaceStylizer. This enables for few-shot on-device face stylization.
To assist customers in adapting MediaPipe FaceStylizer to completely different kinds, we’ve got constructed an end-to-end pipeline. The pipeline features a GAN inversion encoder and an environment friendly face generator mannequin. Customers can fine-tune the mannequin with a couple of examples of the specified model pictures utilizing MediaPipe Mannequin Maker. The fine-tuning course of freezes the encoder module and solely fine-tunes the generator. The generator is educated to reconstruct a picture of an individual’s face within the model of the enter pictures. This enables MediaPipe FaceStylizer to adapt to personalised kinds and stylize take a look at pictures of actual human faces.
The generator utilized in MediaPipe FaceStylizer, known as BlazeStyleGAN, is predicated on the StyleGAN mannequin household. It accommodates a mapping community and a synthesis community. Nevertheless, we’ve got designed a extra environment friendly synthesis community to cut back computational complexity whereas sustaining era high quality. Now we have educated BlazeStyleGAN by distilling it from a trainer StyleGAN mannequin, utilizing a multi-scale perceptual loss and adversarial loss within the distillation course of.
Now we have additionally launched an environment friendly GAN inversion because the encoder to assist image-to-image stylization. The encoder is outlined by a MobileNet V2 spine and educated with pure face pictures.
Now we have documented the mannequin complexities when it comes to parameter numbers and computing FLOPs. BlazeStyleGAN considerably reduces the mannequin complexity in comparison with StyleGAN, whereas sustaining era high quality. Now we have benchmarked the inference time of MediaPipe FaceStylizer on numerous high-end cellular units and achieved real-time efficiency.
The mannequin has been educated with a various dataset of human faces and has been evaluated for equity. It performs properly and is balanced when it comes to human gender, skin-tone, and ages.
Now we have supplied visible samples of face stylization outcomes utilizing MediaPipe FaceStylizer, demonstrating high-quality face stylization for widespread kinds.
MediaPipe Options will likely be releasing MediaPipe FaceStylizer to the general public. Customers can make the most of MediaPipe Mannequin Maker to coach a personalized face stylization mannequin and deploy it to purposes throughout platforms utilizing the MediaPipe Duties FaceStylizer API.
We wish to acknowledge the contributions of assorted groups throughout Google that made this work attainable.
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