Inspired by her hometown in Congo, Anifa was intentional about shedding light on issues facing the Central African country with a short documentary at the start of the show. From mineral site conditions to the women and children who suffer as a result of these issues, Anifa’s mission was to educate before debuting any clothes. “Serving was a big part of who I am, and what I want to do,” she said in the short documentary.
Heh—here’s a super fun application of body tracking tech (see whole category here for previous news) that shows off how folks have been working to redefine what’s possible with. realtime machine learning on the Web (!):
One’s differing physical abilities shouldn’t stand in the way of drawing & making music. Body-tracking tech from my teammates George & Tyler (see previous) is just one of the new Web-based experiments in Creatability. Check it out:
Creatability is a set of experiments made in collaboration with creators and allies in the accessibility community. They explore how creative tools – drawing, music, and more – can be made more accessible using web and AI technology. They’re just a start. We’re sharing open-source code and tutorials for others to make their own projects.
Apropos of Google’s Move Mirror project (mentioned last week), here’s a similar idea:
Kinemetagraph reflects the bodily movement of the visitor in real time with a matching pose from the history of Hollywood cinema. To achieve this, it correlates live motion capture data using Kinect-based “skeleton tracking” to an open-source computer vision research dataset of 20,000 Hollywood film stills with included character pose metadata for each image.
The notable thing, I think, is that what required a dedicated hardware sensor a couple of years ago can now be done plug-in-free using just a browser and webcam. Progress!
Unleash the dank emotes! My teammates George & Tyler (see previous) are back at it running machine learning in your browser, this time to get you off the couch with the playful Move Mirror:
Move Mirror takes the input from your camera feed and maps it to a database of more than 80,000 images to find the best match. It’s powered by Tensorflow.js—a library that runs machine learning models on-device, in your browser—which means the pose estimation happens directly in the browser, and your images are not being stored or sent to a server. For a deep dive into how we built this experiment, check out this Medium post.
My teammates George & Tyler have been collaborating with creative technologist Dan Oved to enable realtime human pose estimation in Web browsers via the open-source Tensorflow.js (the same tech behind the aforementioned Emoji Scavenger Hunt). You can try it out here and read about the implementation details over on Medium.
Ok, and why is this exciting to begin with? Pose estimation has many uses, from interactive installations that react to the body to augmented reality, animation, fitness uses, and more. […]
“Teaching Google Photoshop” has been my working mantra here—i.e. getting computers to see like artists & wield their tools. A lot of that hinges upon understanding the shape & movements of the human body. Along those lines, my Google Research teammates Tyler Zhu, George Papandreou, and co. are doing cool work to estimate human poses in video. Check out the demo below, and see their poster and paper for more details.