Headhash vs Face Jam AI

The first face mixer app with realistic results! Mix your face with your friends or family. Borrow Lady Gaga’s eyes or Justin Bieber’s head. Don’t like your boyfriend’s nose? No problem - give him a nose you do like. Like how your hair looks in one photo but that’s all. Mix your pics and create the perfect you! No one will know :). A headhash is any combination of up to 4 face shots, shaken not stirred. The combinations are really limitless! Headhash lets you either use the iPhone camera or choose from the photo library. Once you get photos in you can start creating headhashes in seconds. Select one image for each facial feature - head, eyes, nose and mouth. You then press "Make a Face" and within seconds these features are hashed together in face space to render a face montage. Create some friend blends or if your mood is a bit darker you can do some face debasing. Share your hashes with others to spread photo fun. You can import as many images as you would like and those images will be placed in the Headhash photo Library. This Library will hold all of your imported photos and you can access it at any time and choose different photos to create your headhash. Note that this is only your personal Library, no one has any access to it, except for you and be sure that Headhash will never distribute your personal photos. So if you have the urge to merge then this is the right app for you!

Choose two faces and combine their styles with AI (generative neural nets) Here are a few ways this app can be used: 1) Hair color Set the "input" photo to the person whose hair color you want to change. Choose a photo of a person with the hair color you want for the "style" photo. Since hair color is a relatively shallow style, styles can be mixed at shallow depths here. Mixing deeper styles will transfer deeper properties from the style photo such as face structure, gender, and head pose. 2) Baby generator Choose photos of both parents and mix their styles. If mixing deep styles here, the gender styles will mix. There is a gender slider to adjust for this. If only mixing shallow styles, the gender will be that of the "input" face. 3) Hair transplant This requires deeper style mixing than hair color. Set the input photo to the person who will receive the hair, and the style photo to someone who will donate the hair. Try to find the shallowest depth that transfers the hair style. This seems to be the 2nd to last and 3rd to last layers in most cases. 4) Cartoons Some cartoon faces will be picked up by the face detector and their styles can be mixed with real people. Fun fact: most of the faces used as inputs in the examples were actually generated from this app! Those people do not actually exist. Note: This app does not collect any face data. All processing happens locally on the device. No data is shared with 3rd parties. No data is even stored locally. The only way to export data from this app is to share the generated image using the button in the lower right corner of the "stylized" image. Additional Charges: The watermarks can be removed by subscribing. Terms of use (EULA): https://www.apple.com/legal/internet-services/itunes/dev/stdeula/ Some of the functionality of this app was adapted from the following research: [1] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems 27 (2014). [2] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [3] Karras, Tero, et al. "Analyzing and improving the image quality of stylegan." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. [4] He, Kaiming, et al. "Momentum contrast for unsupervised visual representation learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. [5] Huang, Yuge, et al. "Curricularface: adaptive curriculum learning loss for deep face recognition." proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020. [6] Richardson, Elad, et al. "Encoding in style: a stylegan encoder for image-to-image translation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. [7] Tov, Omer, et al. "Designing an encoder for stylegan image manipulation." ACM Transactions on Graphics (TOG) 40.4 (2021): 1-14.