| license: apache-2.0 | |
| # Video-Coder Training Data | |
| This repository contains the training data for [Video-Coder: Reinforcing Agentic Coding for Video Generation](). | |
| [**🌐 Project Page**]() | [**📖 Paper**]() | [**💻 Code**]() | |
| # 👀 Intro | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/CYWang735/Video-Coder/main/assets/teaser.png" alt="Video-Coder Overview" width="80%"> | |
| </div> | |
| **Video-Coder** is a code-driven agent for video generation. It iteratively writes, executes, and refines Blender Python programs to construct dynamic 3D scenes and render draft videos, providing structural and spatial guidance for downstream video generators. | |
| We design a dedicated data pipeline and curate two high-quality datasets, **Video-Coder-SFT-10k** and **Video-Coder-RL-4k**, which contain diverse prompts and ground-truth reference videos. | |
| ## Dataset Files | |
| The Video-Coder training data consists of JSON files tailored for different training stages: | |
| * `sft_data.json`: Used for Supervised Fine-Tuning (SFT) cold start. | |
| * `rl_data.json`: Used for Reinforcement Learning (RL) training using GRPO. | |
| ## 🎥 Demo | |
| <div align="center"> | |
| <img src="https://raw.githubusercontent.com/CYWang735/Video-Coder/main/assets/qualitative.png" alt="Inference Process" width="85%"> | |
| </div> | |
| ## Citation | |
| If you find this work helpful for your research, please consider citing: | |
| ```bibtex | |
| ``` |