You can deploy this model and start earning money today!

Amazon-FAR/
deltaworld-kinetics

Name: deltaworld-kinetics
Availability: InStock

Views

No views yet

DeltaWorld (Predictor) — Kinetics-700

DeltaWorld is a generative world model operating on "delta" tokens to efficiently generate diverse plausible futures, as introduced in A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens (CVPR 2026 Highlight).

This repository contains the generative ViT-B predictor trained on Kinetics-700 at 512x512 resolution.

Metrics

Prediction quality, measured by applying downstream task heads to the predicted features. Cells report best-of-20 with mean in parentheses. best selects the sample with lowest DINOv3-feature loss to ground truth; mean averages DINOv3 features across all samples before evaluation.

Method	Horizon	VSPW mIoU (↑)	Cityscapes mIoU (↑)	KITTI RMSE (↓)
Copy last (lower bound)	Short (1 frame)	51.2	53.5	3.76
DeltaWorld	Short (1 frame)	56.1 (54.2)	65.9 (64.3)	2.97 (3.36)
Copy last (lower bound)	Mid (3 frames)	44.3	39.6	4.86
DeltaWorld	Mid (3 frames)	51.0 (45.8)	54.8 (49.8)	3.76 (5.14)

Usage

Requires a trained DeltaTok tokenizer and a frozen DINOv3 ViT-B backbone. Full training and evaluation code is available in the DeltaTok GitHub repository. To evaluate:

bash

python main.py validate -c configs/deltaworld_vitb_dinov3_vitb_kinetics.yaml \
  --model.ckpt_path=path/to/deltaworld-kinetics/pytorch_model.bin \
  --model.network.tokenizer.ckpt_path=path/to/deltatok-kinetics/pytorch_model.bin

Acknowledgements

Citation

bibtex

@inproceedings{kerssies2026deltatok,
  title     = {A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens},
  author    = {Kerssies, Tommie and Berton, Gabriele and He, Ju and Yu, Qihang and Ma, Wufei and de Geus, Daan and Dubbelman, Gijs and Chen, Liang-Chieh},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2026}
}

Amazon-FAR/deltaworld-kinetics