comma.ai Video Compression Challenge

I’m working on comma.ai’s lossy video compression challenge: making a driving video smaller while preserving what the challenge’s segmentation and motion-estimation models see. That means testing both the file size and the effect of compression on those models.

I use comma-lab for experiments, decoders, evaluation tools, and research records. tac contains reusable training, compression, and validation code. I develop across Apple Silicon and Linux GPU environments and check submission archives with the challenge’s evaluator.

Leaderboard submissions

My submissions #107, #110, and #140 have all appeared on the official leaderboard:

• #107, Apogee: an HNeRV-based representation with a deterministic decoder.
• #110: per-frame-pair corrections selected against both segmentation and pose distortion, stored with fixed Huffman coding.
• #140: semantic edits and pose compensation, combined with improvements to lossless coding and the archive format.

These submissions build on credited public work. #110 uses #101’s HNeRV representation; #140 builds on the learned renderer and pose components in #130 and #135, including #133’s contributions. The pull requests describe my optimization, selection, coding, and validation work.

For a more visual introduction to related research, The Witness Machine is an interactive notebook about allocating compression error around what a perception model needs. Its demonstrations are separate from official challenge scores.

The research is ongoing. These leaderboard results apply to the challenge’s fixed video and evaluators.