Add comprehensive dataset card for Light-Syn

This PR adds a comprehensive dataset card for Light-Syn. It includes links to the associated paper, project page, and GitHub repository. It also specifies the `image-to-video` task category and provides sample usage instructions for preparing the dataset for training, as found in the GitHub README.

README.md

@@ -0,0 +1,42 @@
+---
+task_categories:
+- image-to-video
+---
+
+# Light-Syn Dataset
+
+This repository contains the **Light-Syn** dataset, introduced in the paper [Light-X: Generative 4D Video Rendering with Camera and Illumination Control](https://huggingface.co/papers/2512.05115).
+
+**Project Page:** [https://lightx-ai.github.io/](https://lightx-ai.github.io/)
+
+**Code:** [https://github.com/TQTQliu/Light-X](https://github.com/TQTQliu/Light-X)
+
+## Dataset Description
+
+Light-Syn is a degradation-based pipeline with inverse-mapping that synthesizes training pairs from in-the-wild monocular footage. This strategy yields a dataset covering static, dynamic, and AI-generated scenes, ensuring robust training for the Light-X framework, which enables controllable rendering from monocular videos with both viewpoint and illumination control.
+
+## Sample Usage
+
+This dataset is used for training the Light-X model. The following steps outline how to prepare the data and start training as described in the associated GitHub repository.
+
+### 1. Prepare Training Data
+
+Download the dataset.
+
+### 2. Generate Metadata
+
+Generate the metadata JSON file describing the training samples.
+
+```bash
+python tools/gen_json.py -r <DATA_PATH>
+```
+
+Then Update the `DATASET_META_NAME` in your config to the path of the newly generated JSON file.
+
+### 3. Start Training
+
+Begin the training process. Checkpoints will be saved in the `output_train/` directory.
+
+```bash
+bash train.sh
+```