Update README.md

README.md

@@ -17,6 +17,11 @@ PKU-DyMVHumans is a versatile human-centric dataset designed for high-fidelity r
 
 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:
 
@@ -25,47 +30,66 @@ It comprises 32 humans across 45 different dynamic scenarios, each featuring hig
 - **Complex human motion:**  It covers a wide range of special costume performances, artistic movements, and sports activities.
 - **Human-object/scene interactions:** It includes human-object interactions, multi-person interactions and complex scene effects (like 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
 
-### Project Description
+### Agreement
 
-- **Developed by:** Hao Chen, Yuqi Hou, Chenyuan Qu, Irene Testini, Xiaohan Hong, Jianbo Jiao
-- **Funded by:** the Ramsay Research Fund, and the Royal Society Short Industry Fellowship
-- **License:** Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+<b>Note that by downloading the datasets, you acknowledge that you have read the agreement, understand it, and agree to be bound by them: <\b>
+
+- 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.
 
-### Sources
-- **Repository:** Coming Soon
-- **Paper:** https://arxiv.org/abs/2404.00989
 
-## Dataset Statistics
+### 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.
 
-- **Total Videos:** 2,152, split between 464 videos captured using 360 cameras and 1,688 with Spectacles cameras.
-- **Scenes:** 15 indoor and 13 outdoor, totaling 28 scene categories.
-- **Short Clips:** The videos have been segmented into 1,380 shorter clips, each approximately 10 seconds long, totaling
-  around 67.78 hours.
-- **Frames:** 8,579k frames across all clips.
 
 ## Dataset Structure
 
-Our dataset offers a comprehensive collection of panoramic videos, binocular videos, and third-person videos, each pair
-of videos accompanied by annotations. Additionally, it includes features extracted using I3D, VGGish, and ResNet-18.
-Given the high-resolution nature of our dataset (5760x2880 for panoramic and binocular videos, 1920x1080 for
-third-person front view videos), the overall size is considerably large. To accommodate diverse research needs and
-computational resources, we also provide a lower-resolution version of the dataset (640x320 for panoramic and binocular
-videos, 569x320 for third-person front view videos) available for download.
+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. 
 
-<b>In this repo, we provide the lower-resolution version of the dataset. To access the high-resolution version, please
-visit the <a href="https://x360dataset.github.io/">official website</a>.</b>
 
+```
+.
+|--- <case_name>
+|   |--- cams                    
+|   |--- videos
+|   |--- per_view                
+|   |--- per_frame              
+|   |--- data_ngp       
+|   |--- data_NeuS
+|   |--- data_NeuS2
+|   |--- data_COLMAP
+|   |--- <overview_fme_*.png>
+|--- ...
+
+```
 
 
 
 ## BibTeX
 ```
-@inproceedings{chen2024x360,
-  title={360+x: A Panoptic Multi-modal Scene Understanding Dataset},
-  author={Chen, Hao and Hou, Yuqi and Qu, Chenyuan and Testini, Irene and Hong, Xiaohan and Jiao, Jianbo},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+@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}
 }
 ```