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Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling

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Haoyu Wu$^{1*}$, Diankun Wu $^{2*}$, Tianyu He $^{1†}$, Junliang Guo $^{1}$, Yang Ye $^{1}$, Yueqi Duan $^{2}$, Jiang Bian $^{1}$

$^1$ Microsoft Research $^2$ Tsinghua University

($^*$ Equal Contribution. † Project Lead)

Reference

@article{wu2025geometryforcing,
  title={Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling},
  author={Wu, Haoyu and Wu, Diankun and He, Tianyu and Guo, Junliang and Ye, Yang and Duan, Yueqi and Bian, Jiang},
  journal={arXiv preprint arXiv:2507.07982},
  year={2025}
}

Overview

Geometry Forcing (GF) Overview. (a) Our proposed GF paradigm enhances video diffusion models by aligning with geometric features from VGGT~\citep{wang2025vggt}. (b) Compared to DFoT~\citep{dfot}, our method generates more temporally and geometrically consistent videos. (c) While baseline features fail to reconstruct meaningful 3D geometry, GF-learned features enable accurate 3D reconstruction.

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💪Get Started

Setup Environments

conda create -n geometryforcing python=3.10 -y
conda activate geometryforcing
pip install -r requirements.txt

Connect to Weights & Biases:

We use Weights & Biases for logging. Sign up if you don't have an account, and modify wandb.entity in config.yaml to your user/organization name.

Download Checkpoints and Data

  1. Download pretrained checkpiont using huggingface:
bash scripts/hf_download_checkpoints.sh
  1. Download pretrained checkpiont using modelscope:
bash scripts/ms_download_checkpoints.sh
  1. Download and process RealEstate10k dataset to data/real-estate-10k

Generating Videos with Pretrained Models

1. Single Image to Long Video (256 Frames):

bash scripts/eval_geometry_forcing.sh

2. Single Image to Rotation Video (16 Frames):

bash scripts/eval_geometry_forcing_rotation.sh

Training Geometry Forcing

bash scripts/train_geometry_forcing.sh

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