README.md

<div align="center">

<h1>Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling </h1>
<a href="https://www.arxiv.org/abs/2507.07982">
<img src='https://img.shields.io/badge/arxiv-geometryforcing-darkred' alt='Paper PDF'></a>
<a href="https://geometryforcing.github.io/">
<img src='https://img.shields.io/badge/Project-Website-orange' alt='Project Page'></a>


[Haoyu Wu](https://cintellifusion.github.io/)$^{1*}$, 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)

</div>

# 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}
}
```

<!-- include asset/main.png -->

# Overview 
![](main.png)
**Geometry Forcing (GF) Overview.**
(a) Our proposed GF paradigm enhances video diffusion models by aligning with geometric features from VGGT. 
(b) Compared to 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.

# 🚀News

- [2025/9/24] We release code and checkpoint.
- [2025/9/22] [Geometry Forcing](https://geometryforcing.github.io/) is accepted to [NeurIPS 2025 NextVid Workshop](https://what-makes-good-video.github.io/) as an Oral!
- [2025/7/10] We release the paper and the project. 

# 💪Get Started 

## Setup Environments 

```shell
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](https://wandb.ai/login?signup=true) 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: 
```shell
bash scripts/hf_download_checkpoints.sh
```


2. Download pretrained checkpiont using modelscope: 

```shell
bash scripts/ms_download_checkpoints.sh
```

3. Download and process RealEstate10k dataset to  `data/real-estate-10k`

## Generating Videos with Pretrained Models

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

```shell
bash scripts/eval_geometry_forcing.sh
```

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

```shell
bash scripts/eval_geometry_forcing_rotation.sh
```

## Training Geometry Forcing

```shell
bash scripts/train_geometry_forcing.sh
```