The developers of RigNet have shared their trained model on a public link which can be opened by clicking the Download button. The Model Path is the folder where the results of the training are stored Trained ModelĪ Machine Learning tool needs the data from a training session, or it won’t be able to run. Some users may want to run it with the console window on. It may take time because it has to download the whole pytorch library (2 GB). By default, the subdirectory _additional_modules in the addon path is used. If Cuda is found in the system, the Install button can be used to download pytorch to the designated location. Once expanded, the preferences display the system info and the missing required packages. Owners of nVidia hardware can install the CUDA toolkit from the distributor’s page At present prebuilt packages support CUDA 10.1, 10.2 and 11.1. This could change in the future, as the torch-geometric library has recently added cpu support. Anyway, it is different for addons of a bigger application.ĬUDA is a requirement. Installing pytorch can be tricky, and usually is done at the beginning of a coding project, with tools like virtualenv, which is part of python, or conda, a proprietary installer. Install the archive, Neural Rigging is listed in the Rigging section It uses the Package Installer for Python (aka pip) and virtualenv behind the scenes. In the end, I have added an auto-install button to download the missing modules. The script still needs pytorch and pytorch-geometric, but their licenses ( modified BSD and MIT, respectively) allow their inclusion in a free software project.Īs an additional constraint, pytorch must match the CUDA version installed in the system. When the blender foundation backed the project, my first concern was to eliminate all 3d dependencies. The first version of the addon resorted to trimesh and open3d to handle 3d operations, which is redundant inside a full fledged 3d app.īinvox, a stand alone tool used to extract volumetric representations, was problematic too, as a binary, non-open source part of the bundle. With python as a programming language, making it “speak” with blender was no big deal, but the need of 3d party modules made it difficult for everyone to use. So, when the code was made public, I wrote a RigNet addon. When I saw skeletal characters coming out in their presentation, I knew that I wanted something like that in blender. It is licensed under the General Public License Version 3 ( GPLv3), or under a commercial license. RigNet is a Machine Learning solution that can assign a skeleton to a new character, based on extrapolations from a set of examples. Tools that use statistics to automate a procedure fall under the field of Machine Learning, which are more and more widespread with the increase of computing power. This description fits a class of problems that are hard to translate into conventional algorithms, but on which, given enough data, a reasonably accurate statistic can be built. Traditionally, it needs to be performed manually, is different every time, and after a while it looks like doing the same thing over and over. This tool covers the task of assigning deformation bones to a character.
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