Update README.md

README.md

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 # PKU-DyMVHumans
 
+## Sources
+
+Project page：https://pku-dymvhumans.github.io/
+
+Github: https://github.com/zhengxyun/PKU-DyMVHumans
+
+Paper: https://arxiv.org/abs/2403.16080
+
+
 ## 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. 
 
+
+
 ## Key Features
 
-- **High-fidelity human performance**：We construct a professional multi-view system to capture humans in motion, which contains 56/60 synchronous cameras with 1080P or 4K resolution
+- **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.
 
@@ -16,6 +27,7 @@ It comprises 32 humans across 45 different dynamic scenarios, each featuring hig
 
 - **Human-object/scene interactions**: It includes human-object interactions, multi-person interactions and complex scene effects (like smoking).
 
+
 ## Dataset format
 
 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). 
@@ -54,7 +66,7 @@ If you find this repo is helpful, please cite:
 
 @article{zheng2024PKU-DyMVHumans,
   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},
+  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}
 }