Neural scene representation and rendering
I'm surprised people are so blown away by this. It's a cool demonstration, but for this problem you have basically infinite training data. If you can find a latent space of faces this is hardly a stretch, since you already have a fantastic notion of locality in your data (by perturbing the camera). The interesting thing is generalization, which they show in figure 3B and is... ok, I guess. It's not that surprising compared to any of the other VAE stuff people have done (see the morphing scenes, 3D face illumination / rendering and furniture stuff from 2 years ago, for instance). It's also not that surprising compared to ex. the generative scene model RL paper that came out a few months ago (with Doom and the driving game). IMO deep learning research has moved beyond "here's another set of points I can fit a curve to". It really feels like this publication was heavily driven by prestige when most of the innovative stuff was achieved by other groups 2 years ago or more.<p>Ex. how's this different from <a href="https://arxiv.org/pdf/1503.03167.pdf" rel="nofollow">https://arxiv.org/pdf/1503.03167.pdf</a> from 2015?