EvoWorld

January 13, 2026 · View on GitHub

Official implementation of
EvoWorld: Evolving Panoramic World Generation with Explicit 3D Memory

EvoWorld is a generative world model that explicitly reconstructs and utilizes a 3D memory for egocentric video generation. Starting from a single panoramic image and a sequence of camera poses, EvoWorld synthesizes future frames with strong 3D consistency by projecting an evolving 3D point cloud onto future views as conditioning for a video diffusion model.

EvoWorld Pipeline Overview

🔧 Installation
📦 Pretrained Weights & Dataset
⚡ Quickstart
- Single-clip generation
- 3-clip iterative generation
🧪 Inference (full test set)
📊 Evaluation
🏋️ Training
🗺️ Spatial360 Dataset
📎 Acknowledgements

🔧 Installation

git clone git@github.com:JiahaoPlus/EvoWorld.git --recursive
cd EvoWorld
conda create -n evoworld python=3.11
conda activate evoworld
pip install -r requirements.txt
conda install -c conda-forge "cudnn>=9,<10"

📦 Pretrained Weights & Dataset

We have released:

Model weights (Hugging Face): Evoworld_Unity_Curve_Path
Corresponding dataset (Google Drive): Unity_Curve_Path_Data, UE5_Curve_Path_Data

How to use

1. Download model weights and dataset

Download model weights from Hugging Face and place them in the MODELS/ directory.
Download the dataset from Google Drive.
After downloading the dataset, extract it (adjust paths as needed):
```
tar -xvf /path/to/dataset.tar -C data/
```
Update the model path in run_single_segment.sh and run_unified_pipeline.sh to point to your downloaded weights.

2. Run model on provided example

For single clip generation, run:
```
bash run_single_segment.sh
```
For 3-clip iterative generation, run:
```
bash run_unified_pipeline.sh
```
Make sure the model path in these scripts matches your downloaded weights in MODELS/.

3. Run model across the whole test set

Change your model path in inference_unity_curve.sh to the downloaded weights.
Execute:
```
bash inference_unity_curve.sh
```

4. Get evaluation metrics

Run:
```
bash calculate_metrics.sh
```

⚡ Quickstart

Single-clip generation (one clip)

bash run_single_segment.sh

3-clip iterative generation (memory carried across 3 clips)

bash run_unified_pipeline.sh

Notes:

run_single_segment.sh runs a single-clip example.
run_unified_pipeline.sh runs three clips iteratively, reusing the evolving 3D memory.
Ensure the model path inside these scripts points to your downloaded weights in MODELS/.

🗺️ Spatial360 Dataset

We have released the Unity Curve Path and UE5 Curve Path subset — remaining subsets are coming soon!

🏋️ Training

To reproduce our performance, run the train script:

# Train EvoWorld
bash train.sh

🧪 Inference (full test set)

Update your model path in inference_unity_curve.sh and run:

bash inference_unity_curve.sh

📊 Evaluation

Compute evaluation metrics on the outputs:

bash calculate_metrics.sh

📎 Acknowledgements

This codebase builds upon: