README.md

---
language:
- en
- zh
license: c-uda
tags:
- Video
- Multi-viewpoint
viewer: false
---

# <i>PKU-DyMVHumans</i> Dataset

## Overview

PKU-DyMVHumans is a versatile human-centric dataset designed for high-fidelity reconstruction and rendering of dynamic human performances in markerless multi-view capture settings. 

It comprises 32 humans across 45 different dynamic scenarios, each featuring highly detailed appearances and complex human motions. 

### Sources

- **Project page:** https://pku-dymvhumans.github.io
- **Github:** https://github.com/zhengxyun/PKU-DyMVHumans
- **Paper:** https://arxiv.org/abs/2403.16080

### Key Features:

- **High-fidelity performance:** We construct a multi-view system to capture humans in motion, containing 56/60 synchronous cameras with 1080P or 4K resolution.
- **High-detailed appearance:** It captures complex cloth deformation, and intricate texture details, like delicate satin ribbon and special headwear.
- **Complex human motion:**  It covers a wide range of special costume performances, artistic movements, and sports activities.
- **Human-object/scene interactions:** These include human-object interactions, as well as challenging multi-person interactions and complex scene effects (e.g., lighting, shadows, and smoking).

### Benchmark

The objective of our benchmark is to achieve robust geometry reconstruction and novel view synthesis for dynamic humans under markerless and fixed multi-view camera settings, while minimizing the need for manual annotation and reducing time costs. 

This includes **neural scene decomposition**, **novel view synthesis**, and **dynamic human modeling**.



## Dataset Details

### Agreement

Note that by downloading the datasets, you acknowledge that you have read the agreement, understand it, and agree to be bound by them: 

- The PKU-DyMVHumans dataset is made available only for non-commercial research purposes. Any other use, in particular any use for commercial purposes, is prohibited.
- You agree not to further copy, publish or distribute any portion of the dataset.
- Peking University reserves the right to terminate your access to the dataset at any time.


### Dataset Statistics

- **Scenes:** 45 different dynamic scenarios, engaging in various actions and clothing styles. 
- **Actions:** 4 different action types: dance, kungfu, sport, and fashion show.
- **Individual:** 32 professional players, including 16 males, 11 females, and 5 children.
- **Frames:** totalling approximately 8.2 million frames.


## Dataset Structure

For each scene, we provide the multi-view images (`./case_name/per_view/cam_*/images/`), the coarse foreground with RGBA channels (`./case_name/per_view/cam_*/images/`), 
as well as the coarse foreground segmentation (`./case_name/per_view/cam_*/pha/`), which are obtained using [BackgroundMattingV2](https://github.com/PeterL1n/BackgroundMattingV2). 

To make the benchmarks easier compare with our dataset, we save different data formats (i.e., [Surface-SOS](https://github.com/zhengxyun/Surface-SOS), [NeuS](https://github.com/Totoro97/NeuS), [NeuS2](https://github.com/19reborn/NeuS2), [Instant-ngp](https://github.com/NVlabs/instant-ngp), and [3D-Gaussian](https://github.com/graphdeco-inria/gaussian-splatting)) of PKU-DyMVHumans at **Part1** and write a document that describes the data process. 


```
.
|--- <case_name>
|   |--- cams                    
|   |--- videos
|   |--- per_view                
|   |--- per_frame              
|   |--- data_ngp       
|   |--- data_NeuS
|   |--- data_NeuS2
|   |--- data_COLMAP
|   |--- <overview_fme_*.png>
|--- ...

```



## BibTeX
```
@article{zheng2024DyMVHumans,
  title={PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling},
  author={Zheng, Xiaoyun and Liao, Liwei and Li, Xufeng and Jiao, Jianbo and Wang, Rongjie and Gao, Feng and Wang, Shiqi and Wang, Ronggang},
  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}
```