Update README.md

Author: Shimingyi

README.md

@@ -27,13 +27,13 @@
 ## Update log
 
 - (2024.04.24)
-  - Release the windows Unity demo (GPU) trained in 100style dataset.
+  - Release the Windows Unity demo (GPU) trained in 100STYLE dataset.
 - (2024.06.23)
-  - Release the training code in pytorch.
+  - Release the training code in PyTorch.
 
 ## Getting Started
 
-Our project is developed with Unity, and features a real-time character control demo that generate high-quality and diversity character animations, responding in real-time to user-supplied control signals. With our character controller, you can control your character to move with any arbitray style you want, all achieved through a single unified model.
+Our project is developed with Unity, and features a real-time character control demo that generates high-quality and diverse character animations, responding in real-time to user-supplied control signals. With our character controller, you can control your character to move with any arbitrary style you want, all achieved through a single unified model.
 
 A well-designed diffusion model is powering behind the demo, and it can be run efficiently on consumer-level GPUs or Apple Silicon MacBooks. For more information, please visit our project's [homepage](https://aiganimation.github.io/CAMDM/) or the [releases page](https://github.com/AIGAnimation/CAMDM/releases) to download the runnable program.
 
@@ -53,29 +53,29 @@ L:     Previous style
 
 ## Train from Scratch
 
-Our project contains two main modules: network training part with [PyTorch](https://github.com/AIGAnimation/CAMDM/PyTorch/), and demo with [Unity](https://github.com/AIGAnimation/CAMDM/Unity/). Both modules are open-sourced and can be accessed in this repository.
+Our project contains two main modules: Network training part with [PyTorch](https://github.com/AIGAnimation/CAMDM/tree/main/PyTorch), and demo with [Unity](https://github.com/AIGAnimation/CAMDM/tree/main/Unity). Both modules are open-sourced and can be accessed in this repository.
 
 ### Character and Motion Preparation
 
 To train a character animation system, you first need a rigged character and its corresponding motion data. In our project, we provide an example with Y-Bot from [Mixamo](https://www.mixamo.com/#/), which uses the standard Mixamo skeleton configuration. We also retargeted the 100STYLE dataset with the Mixamo skeleton. Therefore, you can download any other character from Mixamo and drive it with our trained model.
 
 For customized character and motion data, please wait for our further documentation to explain the retargeting and rigging process.
 
-### Diffusion Network Training [[PyTorch]](https://github.com/AIGAnimation/CAMDM/PyTorch/) 
+### Diffusion Network Training [[PyTorch]](https://github.com/AIGAnimation/CAMDM/tree/main/PyTorch) 
 
-All the training code and documents can be found in the subfolder of our repository.
+All the training codes and documents can be found in the subfolder of our repository.
 
-A practical training session using the entire 100STYLE dataset will take approximately one day, although acceptable checkpoints can usually be obtained after just a few hours (more than 4 hours). Following the completion of the network training, it's necessary to convert the saved checkpoints into the ONNX format. This allows them to be imported into Unity for use as a learning module. For more detals, please check the subfolder.
+A practical training session using the entire 100STYLE dataset will take approximately one day, although acceptable checkpoints can usually be obtained after just a few hours (more than 4 hours). Following the completion of the network training, it's necessary to convert the saved checkpoints into the ONNX format. This allows them to be imported into Unity for use as a learning module. For more details, please check the subfolder.
 
-### Unity Inference [[Unity]](https://github.com/AIGAnimation/CAMDM/PyTorch/) 
+### Unity Inference [[Unity]](https://github.com/AIGAnimation/CAMDM/tree/main/Unity) 
 TBA.
 
 ## ToDo-List
 
-- [X] Release unity .exe demo in windows. （2024.04.24）
-- [X] Release the training code in pytorch. （2024.06.23）
-- [ ] Release the inference code in unity. （will release before 06.26）
-- [ ] Release the evaluation code in paper. （will release before 06.30）
+- [X] Release Unity .exe demo. （2024.04.24）
+- [X] Release the training code in PyTorch. （2024.06.23）
+- [ ] Release the inference code in Unity. （will release before 06.26）
+- [ ] Release the evaluation code. （will release before 06.30）
 - [ ] Release the inference code to support any character control. (TBA)
 
 ## Acknowledgement