Our librairies and APIs provide developers with an easy way to implement semantic detection on a camera videofeed, in real-time.
For example, by using our “faceFilter” API, a web developer can build a camera-based webapp featuring AR lenses and filters just as Snap or Facebook. All without any knowledge of 3D programming, machine learning / deep-learning or otherwise!
We design and train artificial neural networks, fully programmed in javascript/webGL from our unique framework, to solve very specific problems in the constrained real-life environment. For instance, we trained neural networks to understand human emotions from the webcam of modern smartphones. Launching the API soon, it could be used to build talking avatars (an animoji-like feature) in the browser, for video-conferences or just messaging.
Whether you need help with a problem, or have a brilliant idea to suggest, contact us! We’re all ears and we’ll get back to you asap.
“Congratulations for this incredible library!
Can I get precise coordinates of eyes, mouth and nose?
Let me explain what I want to do … I’m not a blander/threeJs professional and I want to use CSS3 capabilities to position objects on camera using coordinates delivery from jellizFaceFilter library. This is possible?”
“Amazing library! Nice to finally see an alternative to the existing (slow) ones.
Is there any intent to add the option to track multiple faces in the future?”
“I love this API! Thank you! As soon as I’ve built anything, I’ll share it with you.
I have a quick question – can I use this with JSON models only, or is that just your preference?”
“We’re a startup selling prescription glasses online and I was looking at existing code on github that we could use for virtual try on.
I’m simply at a loss for words.. This is exactly what we’ve been looking for. Exactly!”
