Update README.md

README.md

@@ -3,63 +3,109 @@ language:
 - en
 license: "apache-2.0"
 ---
+[Github](https://github.com/KwaiVGI/SynCamMaster) 
+
+[Project Page](https://jianhongbai.github.io/SynCamMaster/) 
+
+[Paper](https://arxiv.org/abs/2412.07760)
 
-# Acknowledgments
-We thank Jinwen Cao, Yisong Guo, Haowen Ji, Jichao Wang, and Yi Wang from Kuaishou Technology for their invaluable help in constructing the SynCamVideo-Dataset.
 
 # Dataset Card
-## 📷 SynCamVideo Dataset
+## 📷 Dataset: SynCamVideo Dataset
 ### 1. Dataset Introduction
-The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using the Unreal Engine 5. It consists of 1,000 different scenes, each captured by 36 cameras, resulting in a total of 36,000 videos. SynCamVideo features 50 different animals as the "main subject" and utilizes 20 different locations from [Poly Haven](https://polyhaven.com/hdris) as backgrounds. In each scene, 1-2 subjects are selected from the 50 animals and move along a predefined trajectory, the background is randomly chosen from the 20 locations, and the 36 cameras simultaneously record the subjects' movements.
 
-The cameras in each scene are placed on a hemispherical surface at a distance to the scene center of 3.5m - 9m. To ensure the rendered videos have minimal domain shift with real-world videos, we constraint the elevation of each camera between 0° - 45°, and the azimuth between 0° - 360°. Each camera is randomly sampled within the constraints described above, rather than using the same set of camera positions across scenes. The figure below shows an example, where the red star indicates the center point of the scene (slightly above the ground), and the videos are rendered from the synchronized cameras to capture the movements of the main subjects (a goat and a bear in the case).
+**TL;DR:** The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses. The SynCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction. The camera is stationary in the SynCamVideo Dataset. If you require footage with moving cameras rather than stationary ones, please explore our [MultiCamVideo](https://huggingface.co/datasets/KwaiVGI/MultiCamVideo-Dataset) Dataset.
+
+<div align="center">
+  <video controls autoplay style="width: 70%;" src="https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/qEUQstpMa3-6UjbG_0ytq.mp4"></video>
+</div>
+
+The SynCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera poses.
+It consists of 3.4K different dynamic scenes, each captured by 10 cameras, resulting in a total of 34K videos. Each dynamic scene is composed of four elements: {3D environment, character, animation, camera}. Specifically, we use animation to drive the character 
+and position the animated character within the 3D environment. Then, Time-synchronized cameras are set up to render the multi-camera video data.
+<p align="center">
+  <img src="https://github.com/user-attachments/assets/107c9607-e99b-4493-b715-3e194fcb3933" alt="Example Image" width="70%">
+</p>
+
+**3D Environment:** We collect 37 high-quality 3D environments assets from [Fab](https://www.fab.com). To minimize the domain gap between rendered data and real-world videos, we primarily select visually realistic 3D scenes, while choosing a few stylized or surreal 3D scenes as a supplement. To ensure data diversity, the selected scenes cover a variety of indoor and outdoor settings, such as city streets, shopping malls, cafes, office rooms, and the countryside.
+
+**Character:** We collect 66 different human 3D models as characters from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com).
+
+**Animation:** We collect 93 different animations from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com), including common actions such as waving, dancing, and cheering. We use these animations to drive the collected characters and create diverse datasets through various combinations.
+
+**Camera:** To enhance the diversity of the dataset, each camera is randomly sampled on a hemispherical surface centered around the character.
+
+### 2. Statistics and Configurations
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/3WEiTpKH9yDjOUn4wonZb.png)
+Dataset Statistics:
 
-The SynCamVideo Dataset can be used to train multi-camera synchronized video generation models, inspiring applications in areas such as filmmaking and multi-view data generation for downstream tasks.
+| Number of Dynamic Scenes | Camera per Scene | Total Videos |
+|:------------------------:|:----------------:|:------------:|
+| 3400                   | 10               | 34,000      |
 
-### 2. File Structure
+Video Configurations:
+
+| Resolution  | Frame Number | FPS                      |
+|:-----------:|:------------:|:------------------------:|
+| 1280x1280   | 81           | 15                       |
+
+Note: You can use 'center crop' to adjust the video's aspect ratio to fit your video generation model, such as 16:9, 9:16, 4:3, or 3:4.
+
+Camera Configurations:
+
+| Focal Length            | Aperture           | Sensor Height | Sensor Width |
+|:-----------------------:|:------------------:|:-------------:|:------------:|
+| 24mm  | 5.0     | 23.76mm       | 23.76mm      |
+
+
+
+### 3. File Structure
 ```
-SynCamVideo
+SynCamVideo-Dataset
 ├── train
-│   ├── videos    # training videos
-│   │   ├── scene1    # one scene
-│   │   │   ├── xxx.mp4    # synchronized 100-frame videos at 480x720 resolution
-│   │   │   └── ...
-│   │   │   ...
-│   │   └── scene1000
-│   │       ├── xxx.mp4
-│   │       └── ...
-│   ├── cameras    # training cameras
-│   │   ├── scene1    # one scene
-│   │   │   └── xxx.json    # extrinsic parameters corresponding to the videos
-│   │   │   ...
-│   │   └── scene1000
-│   │       └── xxx.json
-│   └──caption
-│       └── cogvideox_caption.csv    # caption generated with "THUDM/cogvlm2-llama3-caption"
-└──val
-    └── cameras    # validation cameras
-        ├── Hemi36_4m_0    # distance=4m, elevation=0°
-        │   └── Hemi36_4m_0.json    # 36 cameras: distance=4m, elevation=0°, azimuth=i * 10°
-        │   ...
-        └── Hemi36_7m_45
-            └── Hemi36_7m_45.json
+│   └── f24_aperture5
+│       ├── scene1    # one dynamic scene
+│       │   ├── videos
+│       │   │   ├── cam01.mp4    # synchronized 81-frame videos at 1280x1280 resolution
+│       │   │   ├── cam02.mp4
+│       │   │   ├── ...
+│       │   │   └── cam10.mp4
+│       │   └── cameras
+│       │       └── camera_extrinsics.json    # 81-frame camera extrinsics of the 10 cameras 
+│       ├── ...
+│       └── scene3400
+└── val
+    └── basic
+        ├── videos
+        │   ├── cam01.mp4    # example videos corresponding to the validation cameras
+        │   ├── cam02.mp4
+        │   ├── ...
+        │   └── cam10.mp4
+        └── cameras
+            └── camera_extrinsics.json    # 10 cameras for validation
 ```
 
 ### 3. Useful scripts
+- Data Extraction
+```bash
+tar -xzvf SynCamVideo-Dataset.tar.gz
+```
 - Camera Visualization
 ```python
-python vis_cam.py --pose_file_path ./SynCamVideo-Dataset/val/cameras/Hemi36_4m_0/Hemi36_4m_0.json --num_cameras 36
+python vis_cam.py
 ```
 
-The visualization script is modified from [CameraCtrl](https://github.com/hehao13/CameraCtrl/blob/main/tools/visualize_trajectory.py), thanks for their inspiring work.
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/3WCWS0Axlnu5MyOBqMoVC.png" alt="Example Image" width="40%">
+</p>
 
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/_IHYjTaCt1pUusa1qjQcX.jpeg)
+## Acknowledgments
+We thank Jinwen Cao, Yisong Guo, Haowen Ji, Jichao Wang, and Yi Wang from Kuaishou Technology for their invaluable help in constructing the SynCamVideo-Dataset.
 
-## Citation
+## 🌟 Citation
 
-```bibtex
+Please cite our paper if you find our dataset helpful.
+```
 @misc{bai2024syncammaster,
       title={SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints}, 
       author={Jianhong Bai and Menghan Xia and Xintao Wang and Ziyang Yuan and Xiao Fu and Zuozhu Liu and Haoji Hu and Pengfei Wan and Di Zhang},