soneymathew / infinigen Goto Github PK

Please visit our website, https://infinigen.org

Installation is tested and working on the following platforms:

• Ubuntu 22.04.2 LTS
  • GPUs options tested: CPU only, GTX-1080, RTX-2080, RTX-3090, RTX-4090 (Driver 525, CUDA 12.0)
  • RAM: 16GB
• MacOS Monterey & Ventura, Apple M1 Pro, 16GB RAM

Windows users should install WSL2. More instructions coming soon.

Run these commands to get started

git clone https://github.com/princeton-vl/infinigen.git
conda create --name infinigen python=3.10
conda activate infinigen
bash install.sh

install.sh may take significant time to download Blender3.3 and compile all source files.

Ignore non-fatal warnings. See Getting Help for guidelines on posting github issues

Run the following or add it to your ~/.bashrc (Linux/WSL) or ~/.bash_profile (Mac)

# on Linux/WSL
export BLENDER="/PATH/TO/infinigen/blender/blender"
# on MAC
export BLENDER="/PATH/TO/infinigen/Blender.app/Contents/MacOS/Blender"

git submodule init
git submodule update
sudo apt-get install libglm-dev libglew-dev libglfw3-dev libgles2-mesa-dev zlib1g-dev

bash install.sh opengl

cd infinigen/docker
bash make_docker.sh
docker exec -it infinigen bash
source setup.sh

⚠️ Known issue ⚠️ : We are actively fixing an issue which causes commands not to be reproducible on many platforms. The same command may produce multiple rearranged scenes with different runtimes and memory requirements.

This guide will show you how to generate an image and it's corresponding depth ground-truth, similar to those shown above.

Infinigen generates scenes by running multiple tasks (usually executed automatically, like in Generate image(s) in one command). Here we will run them one by one to demonstrate.

cd worldgen
mkdir outputs

# Generate a scene layout
$BLENDER --noaudio --background --python generate.py -- --seed 0 --task coarse -g desert simple --output_folder outputs/helloworld/coarse

# Populate unique assets
$BLENDER --noaudio --background --python generate.py -- --seed 0 --task populate fine_terrain -g desert simple --input_folder outputs/helloworld/coarse --output_folder outputs/helloworld/fine

# Render RGB images
$BLENDER --noaudio --background --python generate.py -- --seed 0 --task render -g desert simple --input_folder outputs/helloworld/fine --output_folder outputs/helloworld/frames

# Render again for accurate ground-truth
$BLENDER --noaudio --background --python generate.py -- --seed 0 --task render -g desert simple --input_folder outputs/helloworld/fine --output_folder outputs/helloworld/frames -p render.render_image_func=@flat/render_image 

Stdout logs should indicate what the code is working on. Use --debug for even more detail. After each command completes you can inspect it's --output_folder for results, including running $BLENDER outputs/helloworld/coarse/scene.blend or similar to view blender files. We hide many meshes by default for viewport stability; to view them, click "Render" or use the UI to unhide them.

We provide tools/manage_datagen_jobs.py, a utility to run these or similar steps for you on many random seeds.

python -m tools.manage_datagen_jobs --output_folder outputs/hello_world --num_scenes 1 
--pipeline_configs local_16GB monocular blender_gt --specific_seed 0 --configs desert simple

Ready to remove the guardrails? Try the following:

• Swap desert for any of config/scene_types to get different biome (or write your own crazy config!). You can also add in the name of any file in configs.
• Change the --specific_seed to any number to produce different scenes, or remove it and set --num_scenes 50 to try many random seeds.
• Remove simple to generate more detailed (but EXPENSIVE) scenes, as shown in the trailer.
• Read and customize generate.py to understand how infinigen works under the hood.
• Append -p compose_scene.grass_chance=1.0 to the first command to force grass (or any of generate.py's 'run_stage' asset names) to appear in the scene. You can modify the kwargs @gin.configurable() python function in the entire repo via this mechanism.

--configs enables you to customize the random distribution of visual content. If you do not select any config in the folder config/scene_types, the code choose one for you at random.

--pipeline_configs determines what compute resources will be used, and what render jobs are necessary for each scene. A list of configs are available in tools/pipeline_configs. You must pick one config to determine compute type (ie local_64GB or slurm) and one to determine the dataset type (such as monocular or monocular_video). Run python -m tools.manage_datagen_jobs --help for more options related to dataset generation.

If you intend to use CUDA-accelerated terrain (--pipeline_configs enable_gpu), you must run install.sh on a CUDA-enabled machine.

Infinigen uses Google's "Gin Config" heavily, and we encourage you to consult their documentation to familiarize yourself with its capabilities.

Infinigen is an ongoing research project, and has some known issues. Through experimenting with Infinigen's code and config files, you will find scenes which crash or cannot be handled on your hardware. Infinigen scenes are randomized, with a long tail of possible scene complexity and thus compute requirements. If you encounter a scene that does not fit your computing hardware, you should try other seeds, use other config files, or follow up for help.

Infinigen will evolve rapidly over the coming months. Follow us at https://twitter.com/PrincetonVL for updates.

We will add comprehensive tutorials and documentation for all aspects of Infinigen. This README is preliminary, and our docs will be expanded to cover all aspects of the project in detail.

We welcome contributions! You can contribute in many ways:

• Contribute code to this repository - we welcome code contributions. More guidelines coming soon.
• Contribute procedural generators - worldgen/nodes/node_transpiler/dev_script.py provides tools to convert artist-friendly Blender Nodes into python code. Tutorials and guidelines coming soon.
• Contribute pre-generated data - Anyone can contribute their computing power to create data and share it with the community. Please stay tuned for a repository of pre-generated data.

Please post this repository's Github Issues page for help. Please run your command with --debug, and let us know:

• What is your computing setup, including OS version, CPU, RAM, GPU(s) and any drivers?
• What version of the code are you using (link a commit hash), and what if any modifications have you made (new configs, code edits)
• What exact command did you run?
• What were the output logs of the command you ran?
  • If using manage_datagen_jobs, look in outputs/MYJOB/MYSEED/logs/ to find the right one.
  • What was the exact python error and stacktrace, if applicable?