Roy Jara, rjara@ucsd.edu
Elliott Lao, eylao@ucsd.edu
In this project we intend to go through a more abstract path in generating art by using machine learning or more specifically neural networks. With video as an input to a convolutional neural network (an implementation of OpenPose), we generate one output, an (x,y) coordinate pair based on a chosen body part. The x,y coordinate selects the embedding from a pregenerated embedding of visual content, which can be images or sketches pregenerated with another machine learning model. We chose to use StyleGAN to synthesize faces and made a latent space spanning the different combinations of four seeds (actual people). The x,y coordinate is mapped to the embedded space, selecting the current frame which will be the selected frame in the output video. Due to processing power limitations, the output video cannot be generated in real time. However, by presenting the input and output videos side by side the connection between body movement and face shift can be seen.
Regarding the creative goals, we chose to generate video because we thought moving images are always more attractive to the audience. But also because it adds the dimension of time, allowing the input, the user's movements, to move freely around the canvas and change the output however they want - essentially creating a medium of art which is the latent space of the generated embeddings. The user is able of morphing the face however they want and as fast as they want.
This project can be modified to navigate a different latent space of other images, objects, scenes, sketches, etc.
See 188_FINAL_REPORT.pdf
The models tf-openpose and StyleGAN were the default models provided by the respective researchers. We just made modifications to the way we handled the models and the ways to handle output and input.
Your code for generating your project:
- Python:
- run2.py - We made changes to the run.py script included with tf-openpose to receive a directory as an argument instead of the image name, to run the code for all the images in the given directory.
- estimator.py - We altered the estimator.py script included with tf-openpose to write the x,y coordinates of the body parts for each image into a .txt file.
- text_process.py - reads the .txt file with the coordinates for all of the body parts for each frame of the input video, selects the coordinates of only one body part and uses these coordinates to navigate the embeddings of the StyleGAN output. It also iterates over all of the frames to generate the final video with both the input and output.
- Jupyter notebooks:
- face_matrix_gen.ipynb - notebook generating the grid of faces with controllable parameters (seeds, dimensions). Runs on the StyleGAN implementation found in Datahub/github.
- Videos using the same dataset but with different seeds
Any implementation details or notes we need to repeat your work.
- Does this code require other pip packages, software, etc?
- Datahub: StyleGAN example, with the faces dataset.
- tf-openpose can be run on a local computer.
- Requires swig
- Does it run on some other (non-datahub) platform? (CoLab, etc.)
- With pregenerated embeddings, the scripts can be run locally on any computer with python and tf-openpose (and it's dependencies)
References to any papers, techniques, repositories you used:
-
Papers:
- StyleGAN Paper: http://stylegan.xyz/paper
- CMU OpenPose Paper: https://arxiv.org/abs/1812.08008
-
Repositories:
- tf-openpose variation https://github.com/ildoonet/tf-pose-estimation
- StyleGAN https://github.com/NVlabs/stylegan