The goal of this project is transform Isabel into a statue as she performs her song Devices, which is about the feelings of being trapped as someone who suffers from PTSD.
The footage comes from a depthkit shoot I did with Isabel. I have the depth data aligned with RGB data.
I first scavenged for a scrapped dataset of the Metropolitan Museum for busts that I knew Golan had (because I scrapped it for his class when I was a sophomore). I then filtered that by material, taking marble, plaster, stone and leaving the rest. This got me about 400 good pictures to train on. I then ran a face detection algorithm and extracted the faces and added that into the dataset as well. I thought it looked cool so I made this video.
I trained the cycleGAN using the pytorch implementation by the original researches.
I trained it for 200 epochs.
This one I forgot to set the load size to a larger size, so it randomly sampled cropped the picture, hence why it jumps around, but this may be my favorite epoch:
I wasn’t super happy with the results, so I decided to give it a second go, this time experimenting with the parameters of the model more. I had decided to set the weights for the cycles loss function from Isabel-> statues ->Isabel to 100 which would, in comparison make the weight for the loss cycle of statues->Isabel->statues negligible in comparison and thus produce better fake generations of statues given Isabel’s data, but I was wrong.
For CMU’s Art and Machine Learning class Assignment 5 (https://sites.google.com/site/artml2018/) Taught by: Dr. Eunsu Kang Dr. Barnabas Poczos TA: Jonathan Dinu