A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
⚠️ This project is pre-alpha work in progress and subject to extensive change.
We need better data for training and evaluating machine learning systems, especially in the context of unsupervised multi-object video understanding. Current systems succeed on toy datasets, but fail on real-world data. Progress is could be greatly accelerated if we had the ability to create suitable datasets of varying complexity on demand.
- A pipeline for conveniently generating video data.
- Physics simulation for automatically generating physical interactions between multiple objects.
- Good control over the complexity of the generated data, so that we can evaluate individual aspects such as variability of objects and textures.
- Realism: Ideally, the ability to span the entire complexity range from CLEVR all the way to real-world video such as YouTube8. This is clearly not feasible, but we would like to get as close as possible.
- Access to rich ground truth information about the objects in a scene for the purpose of evaluation (eg. object segmentations and properties)
- Control the train/test split to evaluate compositionality and systematic generalization (for example on held-out combinations of features or objects)
To run locally:
- install Blender2.83
- install requirements in the Blender-internal python
- extract KLEVR.zip
blender -noaudio --background --python worker.py -- --assets='/PATH/TO/KLEVR'- (Results are stored in
./output/)
To run on GCP using docker:
make_kubruntu.shto build the required docker imagemake_render.sh localto run the docker container locally- or
make_render.sh remoteto submit a run using the ai-platform - or
make_render.sh hypertuneto launch parallel jobs using the ai-platform
Mainly built on-top of pybullet for physics simulation and Blender for rendering the video. But the code is kept modular to support different rendering backends.
Klaus Greff (Google), Andrea Tagliasacchi (Google and University of Toronto), Derek Liu (University of Toronto), Cinjon Resnick (NYU), Francis Williams (NYU), Issam Laradji (McGill and MILA), Or Litany (Stanford and NVIDIA), Luca Prasso (Google)
This is not an official Google Product