Update README.md

README.md

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 ## 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. 
+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. 
 
-Inspired by recent advancements in neural radiance field (NeRF)-based scene representations, we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. This includes neural scene decomposition, 3D human reconstruction, and novel view synthesis of dynamic scenes.
+It comprises 32 humans across 45 different dynamic scenarios, each featuring highly detailed appearances and complex human motions. 
 
-## Key Features:
-**Part1**:
-**Part1**:
+## 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-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**: It includes human-object interactions, multi-person interactions and complex scene effects (like smoking).
 
 ## Dataset format
 
@@ -32,3 +38,26 @@ To make the benchmarks easier compare with our dataset, we save different data f
 |--- ...
 
 ```
+
+
+## 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**.
+
+## Citation
+
+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},
+  journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2024}
+}
+
+```
+