Zoo Me! vs Face Jam AI

Want to put your face on a bear? Always wondering what you would look if you were a lion? Or a Monkey? Or an Otter! Now see what you would look like with Zoo Me! Put your face on a Zoo animals body! Over 50 great old photos for you stick your face in! Then add awesome captions! Or fun goodies! and stick it all in a frame Then share via facebook, twitter, instagram, e-mail, sms, etc....... Get it now! And if you like it please come back and give it a review. Includes these packs, hundreds of images! 1. Zoo Pack!! 50 animal photos to stick your face in! 2. Goodies pack! 216 images of fun stuff to stick to your picture! 3. Caption tool! Write stuff on your pictures! 4. Frames pack! Put your finished picture in one of over 40 great frames! Quick Instructions - Download it, then open it! - Click an old Picture - Then either take a photo or choose one from your photo album. - If you're using IOS 5 and above the app should find your face and place it in the hole. - Use Multi-touch to move and resize your photo until it's purr-fect! ( Click "Flip" if you wanna flip the pic ) - Click "Color" to adjust the colors of your image to make it match the pic! - When your done click on the goodies tab to add fun stuff to your pic! - Then click frames to add a classic frame to your picture! - Or at any time click the share button in the bottom right and.. - Then you can save it, put it on facebook, twitter, Instagram, photoapplink, or copy it to send your pic in an SMS!

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.