Open Source
SEED is committed to advancing game technology and contributing to the broader research community.
By providing open-source software, SEED enables researchers and developers to access cutting-edge tools and innovations. This initiative reflects SEED's dedication to giving back to the community, fostering collaboration, and driving progress in game development and interactive experiences.
Discover how SEED's contributions are shaping the future of gaming and technology.
AVA Capture
AVA Capture is a distributed system to control and record several cameras from a central UI.
Constant-Time Stateless Shuffling and Grouping
Source code to accompany the related SEED blog post about using format-preserving encryption to shuffle items, or group them together in arbitrary group sizes.
Dem Bones
An implementation of Smooth Skinning Decomposition with Rigid Bones, an automated algorithm to extract the linear blend skinning with bone transformations from a set of example meshes.
Filter-Adapted Spatiotemporal Sampling for Real-Time Rendering
FastNoise generates noise textures optimized towards specific spatial and temporal filters, with specific per-pixel data types.
GATA: Multi-Theme Generative Adversarial Terrain Amplification
Source code supporting the paper "Multi-Theme Generative Adversarial Terrain Amplification" from SIGGRAPH Asia 2019.
GENEA Challenge
Data, code, and results from the GENEA Challenge, a benchmark for data-driven automatic co-speech gesture generation. Contributions to the code from SEED.
Gigi
A tool for rapidly prototyping rendering techniques as a node graph. Includes a visual editor, viewer, and compiler.
Machine Learning for Game Devs
Code repository for the related three-part SEED blog series that covers neural networks, weights & biases, and training learning systems.
Position-Based MPM
An accessible WebGPU implementation of Position-Based Material Point Method from SIGGRAPH 2024.
Project PICA PICA Assets
Assets used during the creation of Project PICA PICA, a real-time ray tracing experiment featuring self-learning agents.
Rig Inversion by Training a Differentiable Rig Function
An example of using the technique described in the paper "Rig Inversion by Training a Differentiable Rig Function" from SIGGRAPH Asia 2022.
Towards Interactive Training of Non-Player Characters in Video Games
Two examples of Markov Ensemble as discussed in the paper "Towards Interactive Training of Non-Player Characters in Video Games" from the 2019 ICML Workshop on Human in the Loop Learning.