The shortened HN title here is nonsensical.<p>In this context, "joint" means "together" or "at the same time". The work's title, "Joint 3D Face Reconstruction and Dense Alignment", essentially means "Face Reconstruction with simultaneous Dense Alignment". The HN title should mention both parts, or remove the word "joint".
University of Basel has some nice project for 3d Modeling. They also have a scala project for 3d face model construction using Gaussian Processes. All the recent 3d modeling papers use Basel Face Model.<p><a href="https://www.futurelearn.com/courses/statistical-shape-modelling/" rel="nofollow">https://www.futurelearn.com/courses/statistical-shape-modell...</a><p><a href="http://gravis.dmi.unibas.ch/PMM/" rel="nofollow">http://gravis.dmi.unibas.ch/PMM/</a><p><a href="https://github.com/unibas-gravis/scalismo-faces" rel="nofollow">https://github.com/unibas-gravis/scalismo-faces</a>
New meme?
<a href="https://github.com/YadiraF/PRNet/raw/master/Docs/images/reconstruct.jpg" rel="nofollow">https://github.com/YadiraF/PRNet/raw/master/Docs/images/reco...</a>
I just realized what this would be great for: Avatars in VR games. Snap a photo or video of your face and have it put straight into your character complete with animations. Excellent.
Nice project! I've been dabbling in DL for about a year (skimmed Stanford CNN course, Silver's RNN course, Andrew Ng's old ML course, etc.). While I can recreate basic stuff like MNIST, I don't feel like I can attack a problem like Pose estimation yet. How long does it take? Is it about diving deep into a single problem? Is it worth doing a nanodegree to shore up gaps?
Good READMEs are important and this is a great example of a good README. The gif at the top alone instantly tells you a lot about what the project is about.