explaingit

mira-wm/mira

Analysis updated 2026-05-18

446PythonAudience · researcherComplexity · 5/5LicenseSetup · hard

TLDR

A research AI system that simulates a real time, playable Rocket League match by generating video frames from all four players' controller inputs.

Mindmap

mindmap
  root((MIRA))
    What it does
      Simulates Rocket League
      Generates video frames
      Real time 2v2 play
    Tech stack
      Python
      PyTorch
      Diffusion model
      DINOv3 encoder
    Use cases
      World model research
      Game simulation
      Multiplayer AI research
    Audience
      ML researchers
      Game AI engineers
      Students

Code map

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What do people build with it?

USE CASE 1

Try the live demo of an AI-generated, playable Rocket League match.

USE CASE 2

Study how a diffusion based world model can simulate a real time multiplayer game.

USE CASE 3

Train a single-player or four-player world model on the released Rocket League dataset.

USE CASE 4

Use the released dataset of synchronized multiplayer gameplay for other research.

What is it built with?

PythonPyTorchHydrapixiDINOv3

How does it compare?

mira-wm/miralzh-phd/topic-feasibility-screenerbuckyroberts/ella
Stars446446449
LanguagePythonPythonPython
Last pushed2023-11-09
MaintenanceDormant
Setup difficultyhardmoderateeasy
Complexity5/53/52/5
Audienceresearcherresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1day+

Requires an NVIDIA GPU, the pixi package manager, and separately downloading Meta's gated DINOv3 weights for codec training.

Released under the Apache License 2.0, which allows free use, modification, and commercial use as long as you keep the license and copyright notice.

In plain English

MIRA is a research project that builds an AI model able to simulate a video game in real time. Specifically, it recreates Rocket League, a soccer game played with rocket powered cars, using a large diffusion model that generates each video frame on the fly based on what all four players in a match are doing with their controls. A full two versus two match can be played entirely inside this simulated model at twenty frames per second on a single graphics card, and there is a live demo online where people can try it. The project is a joint release from three organizations working together on AI research, and it comes with a technical paper describing how the system was built. Under the hood it combines a video encoder, called a representation autoencoder, with a diffusion model that predicts what the next frame should look like given the players' actions. The repository includes code to install the project using a tool called pixi, which sets up the environment and installs dependencies automatically, though it requires an NVIDIA graphics card. It also provides access to the training dataset used for the project, hosted on Hugging Face, which consists of four second clips from real matches, each with synchronized video from every player, their keyboard inputs, and the underlying game state such as ball position, car positions, and score. There is a small tool to explore this dataset interactively. Beyond just using the released model, the code lets researchers train their own version from scratch, including a single player version and a four player version that builds on it. Training the video encoding step requires a separate frozen model, DINOv3, whose weights must be downloaded from Meta directly under Meta's own terms. This is a research codebase aimed at machine learning researchers and engineers who are comfortable with Python and GPU training pipelines, not a ready-to-use consumer application. The code itself is released under the Apache License 2.0, a permissive open source license that allows free use, including commercial use.

Copy-paste prompts

Prompt 1
Explain how a diffusion model can generate playable video game frames in real time.
Prompt 2
Help me set up the pixi environment to install and test MIRA on my NVIDIA GPU.
Prompt 3
Walk me through loading a clip from the rocket-science dataset with RocketScienceDataset.
Prompt 4
Show me how to train the single-player world model using the scripts in this repo.

Frequently asked questions

What is mira?

A research AI system that simulates a real time, playable Rocket League match by generating video frames from all four players' controller inputs.

What language is mira written in?

Mainly Python. The stack also includes Python, PyTorch, Hydra.

What license does mira use?

Released under the Apache License 2.0, which allows free use, modification, and commercial use as long as you keep the license and copyright notice.

How hard is mira to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is mira for?

Mainly researcher.

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