We also encourage users to share their Volume reconstructions socially using the share icon at the top. Our goal with opening our API to the public via our new website is also learning what are the tendencies, trends, and desires people have for such functionality. We do however use it to evaluate the machine learning models to get a better understanding of what works and what doesn’t. It is important to say, we do not share any of the data users upload. It is still work in progress, but we hope to keep on training the networks to acquire more knowledge required to reconstruct a 3D human figures and environments from 2D images and videos.įleisher also told us that users, by trying the experiment for themselves, were providing Volume with valuable feedback:

This is quite different from other approaches that accumulate a lot of knowledge into one single machine learning model. Once we know what to expect, we are able to pick a model which has a more refined understanding of how this should look like in 3D.

While the mechanics of constructing and training such deep Neural Networks have similarities, our mission to create a model that understands the human body and environments requires a lot of data… In the background, we are training a few models for different tasks and developing a classification method for identifying the dominant elements in the image. It is different in some ways and similar in others. We asked Fleisher, who you may also remember as the creator of an AI that gives any webcam Kinect-like capabilities, if training an AI to convert any type of image presents a different challenge than training one to convert something specific: We’ve seen similar AI before, but this one does more than facial reconstruction. The developers of that AI, Or Fleisher and Shirin Anlen, recently updated their website with an interactive AI experiment that converts any flat imageto 3D. In January we wrote about an AI called Volume that ripped characters out of videos.

2d to 3d video converter online