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CompVis/
myriad

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2604.09527

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MYRIAD (Envisioning the Future, One Step at a Time)

MYRIAD (Motion hYpothesis Reasoning via Iterative Autoregressive Diffusion) is an autoregressive diffusion model that predicts open-set future scene dynamics as step-wise inference over sparse point trajectories. Starting from a single image, it can efficiently explore thousands of plausible future outcomes, maintaining physical plausibility.

Paper and Abstract

The MYRIAD model was presented in the paper Envisioning the Future, One Step at a Time.

From a single image, MYRIAD predicts distributions over sparse point trajectories autoregressively. This allows the model to predict consistent futures in open-set environments and plan actions by exploring a large number of counterfactual interactions.

MYRIAD predicts distributions over potential motion auto-regressively

From a single image, our model envisions diverse, physically consistent futures by predicting sparse point trajectories step by step.

Sampling from MYRIAD enables planning-by-search

Its efficiency enables exploring thousands of counterfactual rollouts directly in motion space - here illustrated for billiards planning, where candidate shots are evaluated by simulating many possible outcomes.

Usage

For programmatic use, the simplest way to use MYRIAD is via torch.hub:

python

import torch

# Load the open-set model
myriad_openset = torch.hub.load("CompVis/myriad", "myriad_openset")

# Load the billiard-specific model
myriad_billiard = torch.hub.load("CompVis/myriad", "myriad_billiard")

If you wish to integrate MYRIAD in your own codebase, you can copy model.py and dinov3.py from the GitHub repository. The MyriadStepByStep class contains a predict_simulate method for unrolling trajectories and a low-level forward method to predict distributions for previously observed trajectories.

Citation

If you find our model or code useful, please cite our paper:

bibtex

@inproceedings{baumann2026envisioning,
  title={Envisioning the Future, One Step at a Time},
  author={Baumann, Stefan Andreas and Wiese, Jannik and Martorella, Tommaso and Kalayeh, Mahdi M. and Ommer, Bjorn},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}