[rirarobo]

Very cool work, I'm happy to see more people thinking about deep networks along these lines. It seems that this is very similar to a recent work put on arxiv back in November,

"Learning to Generate Chairs with Convolutional Neural Networks". http://arxiv.org/abs/1411.5928

They also have a very cool video of the generation process: https://youtu.be/QCSW4isBDL0

It's very interesting to see two groups independently developing almost identical networks for inverse graphics tasks, both using pose, shape, and view parameters to guide learning. I think that continuing in this direction could provide a lot of insight into how these deep networks work, and lead to new improvements for recognition tasks too.

@tejask - You should probably cite the above paper, and thanks for providing code! awesome!

[tejask]

thanks for the references! I like that many people are doing such things. After looking at the chairs paper, it seems like they render images given pose,shape,view etc (supervised setting). However, in our model, there is a twist as it is trained either completely unsupervised or biased to separate those variables (but it is never given the true values of those parameters ... just raw data).