Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Phase-Functioned Neural Networks for Character Control
| Date | Stars |
|---|---|
| 2026-07-31 | 523 |
| 2026-08-01 | 523 |
| 2026-08-02 | 523 |
| 2026-08-04 | 524 |
| 2026-08-06 | 524 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
Phase-Functioned Neural Networks for Character Control
======================================================
This project contains the code and data for Phase-Functioned Neural Networks
for Character Control as presented at SIGGRAPH 2017, with the project page
found below:
http://theorangeduck.com/page/phase-functioned-neural-networks-character-control
This paper presents a character controller driven by a special kind of network
called a Phase-Functioned Neural Network which learns a mapping from the user
input parameters, the character's previous state, and the surrounding
environment to the pose of the character at a given frame. The rest of this
README will assume you have read the paper and understand the basic workings
and processes involved in this technique.
The code is essentially separated into two sub-projects.
The first project is a set of python scripts written in Numpy/Scipy/Theano
which preprocess the motion data, generate a database, and train the phase
functioned neural network to perform the regression. The output of this project
are the network weights saved as simple binary files.
The second project (contained in the subfolder "demo") is a C++ project which
contains a basic runtime that loads the network weights and runs an interactive
demo which shows the results of the trained network when controlled via a
game-pad.
Below are details of the steps required for reproducing the results from the
paper from preprocessing and training all the way to runnning the demo.
Installation
------------
Before you do anything else you will first need to install the following python
packages `numpy`, `scipy`, `Pillow`, `theano` as well as `CUDA`, `cuDNN` etc.
This project was built using Python3 but may work with Python2 given a few
minor tweaks.
Preprocessing
-------------
The next step is to build the database of terrain patches which are later
fitted to the motion. For this simply run the following.
python generate_patches.py
This will sample thousands of patches from the heightmaps found in
`data/heightmaps` and store them in a database called `patches.npz`. This
should take a good few minutes so be patient.
Now you can begin the process of preprocessing the animation data - converting
it into the correct format and fitting the terrain to each walk cycle. For this
you run the following:
python generate_database.py
This process can take some time - at least a couple of hours. It uses all the
data found in `data/animations` and once complete will output a database called
`database.npz`. If you want to change the parameterisation used by the network
this is probably the place to look - but note that the preprocessing for this
work is quite fiddily and complicated so you must be careful when you edit this
script not to introduce any bugs. You will also have to remember to update the
runtime to match.
Training
--------
Once you've generated `database.npz` you can begin training. For this simply
run the following:
python train_pfnn.py
Assuming you've installed `theano`, `CUDA`, and everything else successfully
this should start training the neural network. This requires quite a lot of RAM
as well as VRAM. If you get any kind of memory error you can perhaps try using
less data by subsampling the database or even taking some of the clips out of
the preprocessing stage by removing them from `generate_database.py`.
During the training process the weights of the network will be saved at each
epoch to the location `demo/network/pfnn` so don't worry about stopping the
training early. It is possible to achieve decent results in just an hour or so
of training, but for the very best results you may need to wait overnight. For
this reason it might be worth making a backup of the pre-trained demo weights
in `demo/network/pfnn` before beginning training.
Runtime
-------
With the network weights generated you're now ready to try the runtime. For
instructions for this plExcerpt of 4,045 characters
Read on GitHub
7
6
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cb41964b2cf3db97, llm:Repository title and description: 'Phase-Functioned Neural Networks for Character Control' (PFNN) — implementation in Python for character control.
matched fp:cb41964b2cf3db97, llm:Repository title and description: 'Phase-Functioned Neural Networks for Character Control' (PFNN) — implementation in Python for character control.