FaceApp vs Face Jam AI

FaceApp makes photo editing simple and natural. Apply subtle adjustments that fit your style while keeping results realistic. Edit photos quickly and easily - ready for sharing. Use a fantastic set of filters, backgrounds, effects, and other tools to create a seamless and photorealistic edit in ONE TAP. You will never have to spend hours photoshopping again! More than 60 highly photorealistic filters PHOTO EDITOR - Perfect your selfies with Impression filters - Add a beard or mustache - Change your hair color and hairstyle - Add volume to your hair - Try trendy full makeup filters - Creative light effects - Remove acne and blemishes - Smooth wrinkles - Easily enlarge or minimize facial features - Try out the color lens - Easy Compare tool at every step to compare before & after - Total control of temperature, saturation, and more - Discover your best hairstyle and color with our advanced tool READY TO SHARE? Share your FaceApp edits directly to your favorite social media accounts Just ONE tap and your photo is ready for a total social media blitz! - Terms of Use: https://www.faceapp.com/terms - Privacy Policy: https://www.faceapp.com/privacy - Online Tracking Opt-Out Guide: https://www.faceapp.com/online-tracking-opt-out-guide You are welcome to contact us at support.ios@faceapp.com

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.