Add Hugging Face Spaces metadata to README

README.md

@@ -1,101 +1,43 @@
-## Sci-Fi: Symmetric Constraint for Frame Inbetweening
-<h5>Liuhan Chen<sup>1</sup>, <a href='https://vinthony.github.io'>Xiaodong Cun</a><sup>2,*</sup>, <a href='https://xiaoyu258.github.io/'>Xiaoyu Li</a><sup>3</sup>, Xianyi He<sup>1,4</sup>, Shenghai Yuan<sup>1,4</sup>,  Jie Chen<sup>1</sup>, Ying Shan<sup>3</sup>, Li Yuan<sup>1,*</sup></h5>
+---
+title: Sci-Fi Frame Inbetweening
+emoji: 🎬
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: gradio_inference.py
+pinned: false
+license: apache-2.0
+---
 
-<sup>1</sup>Shenzhen Graduate School, Peking University &nbsp;&nbsp;&nbsp; <sup>2</sup><a href='https://gvclab.github.io'>GVC Lab, Great Bay University</a>  &nbsp;&nbsp;&nbsp; 
-<sup>3</sup>ARC Lab, Tencent PCG &nbsp;&nbsp;&nbsp; <sup>4</sup>Rabbitpre Intelligence
+# Sci-Fi: Symmetric Constraint for Frame Inbetweening
 
-We have updated our paper with a new version and chane the name of our framework from Sci-Fi to EF-VI.
-**[Arxiv](https://arxiv.org/abs/2505.21205) | [PDF](https://arxiv.org/pdf/2505.21205)**
+Generate smooth frame interpolation videos using CogVideoX-5B with EF-Net.
 
-## Video demos
-[![Video demos of our Sci-Fi](overview/video_demos.png)](https://youtu.be/_YfFH-uNYQk)
+## Features
 
-[or click here to download the compressed version](overview/video_demos.mp4) 
+- 🎬 High-quality frame inbetweening
+- ⚡ GPU-accelerated inference with Spaces GPU
+- 🎨 Customizable generation parameters
+- 📊 Real-time progress tracking
 
-## Method comparison
-<div align="center">
-  <img src="overview/comparison.png" width="720" alt="Comparison">
-</div>
-<strong>(a)</strong> In current I2V-DM-based methods, the end-frame constraint is weaker than the start-frame constraint due to the same injection mechanism but a smaller training scale, causing a distorted predicted path with collapsed content.<br><be> <strong>(b)</strong> Our Sci-Fi maintains start frame processing while enhancing end-frame constraint injection. This achieves symmetric start-end-frame constraints with small training, yielding a fine predicted path close to the real one with smoother inbetweening.
+## Usage
 
-## Some challenging examples of our Sci-Fi for frame inbetweening.
-<table class="center">
-    <tr style="font-weight: bolder;text-align:center;">
-        <td>Start frame</td>
-        <td>End frame</td>
-        <td>Generated video</td>
-    </tr>
-  	<tr>
-	  <td>
-	    <img src=example_input_pairs/input_pair1/start.jpg width="250">
-	  </td>
-	  <td>
-	    <img src=example_input_pairs/input_pair1/end.jpg width="250">
-	  </td>
-	  <td>
-	    <img src=example_output_gifs/input_pair1.gif width="250" loop="infinite">
-	  </td>
-  	</tr>
-  	<tr>
-	  <td>
-	    <img src=example_input_pairs/input_pair2/start.jpg width="250">
-	  </td>
-	  <td>
-	    <img src=example_input_pairs/input_pair2/end.jpg width="250">
-	  </td>
-	  <td>
-     	    <img src=example_output_gifs/input_pair2.gif width="250">
-	  </td>
-  	</tr>
-         <tr>
-	  <td>
-	    <img src=example_input_pairs/input_pair3/start.jpg width="250">
-	  </td>
-	  <td>
-	    <img src=example_input_pairs/input_pair3/end.jpg width="250">
-	  </td>
-	  <td>
-     	    <img src=example_output_gifs/input_pair3.gif width="250">
-	  </td>
-  	</tr>
-	<tr>
-	  <td>
-	    <img src=example_input_pairs/input_pair4/start.jpg width="250">
-	  </td>
-	  <td>
-	    <img src=example_input_pairs/input_pair4/end.jpg width="250">
-	  </td>
-	  <td>
-     	    <img src=example_output_gifs/input_pair4.gif width="250">
-	  </td>
-  	</tr>
-</table >
+1. **Setup Tab**: Load the model pipeline (required first step)
+2. **Generate Tab**: Upload start and end frames, enter a prompt
+3. **Advanced Settings**: Fine-tune generation parameters
 
-## Deployment for personal use
-### 1. Setup the repository and environment
-```
-git clone https://github.com/GVCLab/Sci-Fi.git
-cd Sci-Fi
-conda create -n Sci-Fi python==3.12
-conda activate Sci-Fi
-pip install -r requirements.txt
-```
-### 2. Download checkpoint
-Download the CogVideoX-5B-I2V model (due to fine-tuning, the weights of the transformer denoiser are different from the original) and EF-Net.
-The weights are available at [🤗HuggingFace](https://huggingface.co/LiuhanChen/Sci-Fi) and [🤖ModelScope](https://www.modelscope.cn/models/clhxclh/Sci-Fi).
+## Model Details
 
-### 3. Launch the inference script!
-The example input keyframe pairs are in `examples/` folder, and 
-the corresponding generated videos (720x480, 49 frames) are placed in `outputs/` folder.
-</br>
-To interpolate, run:
-```
-bash Sci_Fi_frame_inbetweening.sh
-```
+- **Base Model**: CogVideoX-5B-I2V
+- **EF-Net**: Custom enhancement network for symmetric frame constraints
+- **Output**: 49 frames at 7 FPS (720x480)
 
 ## Citation
-🌟 If you find our work helpful, please leave us a star and cite our paper.
-```
+
+If you find this work helpful, please cite:
+
+```bibtex
 @article{chen2025sci,
   title={Sci-Fi: Symmetric Constraint for Frame Inbetweening},
   author={Chen, Liuhan and Cun, Xiaodong and Li, Xiaoyu and He, Xianyi and Yuan, Shenghai and Chen, Jie and Shan, Ying and Yuan, Li},
@@ -103,3 +45,14 @@ bash Sci_Fi_frame_inbetweening.sh
   year={2025}
 }
 ```
+
+## Links
+
+- 📄 [Paper](https://arxiv.org/abs/2505.21205)
+- 🐙 [GitHub](https://github.com/GVCLab/Sci-Fi)
+- 🤗 [Model Weights](https://huggingface.co/LiuhanChen/Sci-Fi)
+
+## Requirements
+
+- Model weights must be downloaded and placed in the appropriate directory
+- See the Setup tab for configuration details