ICCV 2017 this year had dozens of papers using GANs and other methods to make simulated images/videos look more realistic. The hunger for cheap usable data is real.
The notable exception is if you want to generate segmentation data (where you want labels for each pixel), in this case the set of features you want is essentially an image already.
ICCV 2017
* Photographic Image Synthesis with Cascaded Refinement Networks: https://arxiv.org/pdf/1707.09405.pdf
* Synthetic avatars: https://arxiv.org/pdf/1704.05693.pdf
* Synthesizing cell microscopy data: https://arxiv.org/pdf/1708.04692.pdf
* Material Editing Using a Physically Based Rendering Network: https://arxiv.org/pdf/1708.00106.pdf
* 3D-PRNN: Generating Shape Primitives With Recurrent Neural Networks:
* for more search 'generative' 'generating' 'simulating' 'synthesis' 'synthetic' on: http://iccv2017.thecvf.com/program/main_conference#schedule
Also worth mentioning:
CVPR 2017 best paper, this is the other exception, it's unique because it doesn't require _any_ labeled data
* Learning from Simulated and Unsupervised Images through Adversarial Training: https://arxiv.org/abs/1612.07828