Update README.md

README.md

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 ## 📷 MultiCamVideo Dataset
 ### 1. Dataset Introduction
 
-**TL;DR:** The MultiCamVideo Dataset, introduced in [ReCamMaster](https://arxiv.org/abs/2503.11647), is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and its corresponding camera trajectories. The MultiCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction.
+**TL;DR:** The MultiCamVideo Dataset, introduced in [ReCamMaster](https://arxiv.org/abs/2503.11647), is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera trajectories. The MultiCamVideo Dataset can be valuable in fields such as camera-controlled video generation, synchronized video production, and 3D/4D reconstruction.
 
 <div align="center">
   <video controls autoplay style="width: 70%;" src="https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/r-cc03Z6b5v_X5pkZbIZR.mp4"></video>
 </div>
 
-The MultiCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and its corresponding camera trajectories.
-It consists of 13.6K different dynamic scenes, each captured by 10 cameras, resulting in a total of 136K videos. Each dynamic scene is composed of four elements: {3D environment, character, animation, camera}. Specifically, we use animation to drive the character, 
-and positioning the animated character within the 3D environment. Then, Time-synchronized cameras are then set up to move along predefined trajectories to render the multi-camera video data.
+The MultiCamVideo Dataset is a multi-camera synchronized video dataset rendered using Unreal Engine 5. It includes synchronized multi-camera videos and their corresponding camera trajectories.
+It consists of 13.6K different dynamic scenes, each captured by 10 cameras, resulting in a total of 136K videos. Each dynamic scene is composed of four elements: {3D environment, character, animation, camera}. Specifically, we use animation to drive the character 
+and position the animated character within the 3D environment. Then, Time-synchronized cameras are set up to move along predefined trajectories to render the multi-camera video data.
 
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/6530bf50f145530101ec03a2/Ea0Feqy7uBTLczyPal-CE.png" alt="Example Image" width="70%">
 </p>
 
-**3D Environment:** We collect 37 high-quality 3D environments assets from [Fab](https://www.fab.com). To minimize the domain gap between rendered data and real-world videos, we primarily select visually realistic 3D scenes, while choosing a few stylized or surreal 3D scenes as a supplement. To ensure data diversity, the select scenes cover a variety of indoor and outdoor settings, such as city streets, shopping malls, cafes, office rooms, and countryside.
+**3D Environment:** We collect 37 high-quality 3D environments assets from [Fab](https://www.fab.com). To minimize the domain gap between rendered data and real-world videos, we primarily select visually realistic 3D scenes, while choosing a few stylized or surreal 3D scenes as a supplement. To ensure data diversity, the selected scenes cover a variety of indoor and outdoor settings, such as city streets, shopping malls, cafes, office rooms, and the countryside.
 
 **Character:** We collect 66 different human 3D models as characters from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com).
 
-**Animation:** We collect 93 different animations from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com), including common actions such as waving, dancing, and cheering. We use these animations to drive the collected characters and created diverse datasets through various combinations.
+**Animation:** We collect 93 different animations from [Fab](https://www.fab.com) and [Mixamo](https://www.mixamo.com), including common actions such as waving, dancing, and cheering. We use these animations to drive the collected characters and create diverse datasets through various combinations.
 
 **Camera:** To ensure camera movements are diverse and closely resemble real-world distributions, we create a wide range of camera trajectories and parameters to cover various situations. To achieve this by designing rules to batch-generate random camera starting positions and movement trajectories: