Under the hood 🧠

Orbis uses a hybrid tokenizer with semantic + detail tokens that work in both continuous and discrete spaces.
The world model then predicts the next frame by gradually denoising or unmasking it, using past frames as context.

Realistic and Diverse Rollouts 4/4

Realistic and Diverse Rollouts 3/4

Realistic and Diverse Rollouts 2/4

Realistic and Diverse Rollouts 1/4

While other models drift or blur on turns, Orbis stays on track — generating realistic, stable futures beyond the training horizon.

On our curated nuPlan-turns dataset, Orbis achieves better FVD, precision, and recall, capturing both visual and dynamics realism.

We ask how continuous vs. discrete models and their tokenizers shape long-horizon behavior.

Findings:
Continuous models (Flow Matching) are
• Far less brittle to design choices
• Produce realistic, stable rollouts up to 20s
• And generalize better to unseen driving conditions

Continuous > Discrete

Driving world models look good for a few frames, then they drift, blur, or freeze, especially when a turn or complex scene appears. These failures reveal a deeper issue: models aren’t capturing real dynamics. We introduce new metrics to measure such breakdowns.

Our work Orbis goes to #NeurIPS2025!

A continuous autoregressive driving world model that outperforms Cosmos, Vista, and GEM with far less compute.

469M parameters
Trained on ~280h of driving videos

📄 arxiv.org/pdf/2507.13162
🎬 lmb-freiburg.github.io/orbis.github...
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