Add metadata and improve model card

This PR improves the model card by:
- Adding the `image-to-3d` pipeline tag and the `cc-by-nc-sa-4.0` license to the metadata.
- Adding a link to the official GitHub repository.
- Shortening the abstract for better readability.
- Including a "Quick Start" section with installation and inference instructions found in the repository.

README.md

@@ -1,15 +1,22 @@
+---
+license: cc-by-nc-sa-4.0
+pipeline_tag: image-to-3d
+---
+
 # Motion 3-to-4: 3D Motion Reconstruction for 4D Synthesis
 
-**Motion 3-to-4** reconstructs 3D motion from video inputs for 4D synthesis, enabling the generation of animated 3D models with realistic motion.
+**Motion 3-to-4** reconstructs 3D motion from video inputs for 4D synthesis, enabling the generation of animated 3D models with realistic motion in a feed-forward manner.
 
-[Paper](https://arxiv.org/abs/2601.14253) | [Project Page](https://motion3-to-4.github.io/)
+[Paper](https://arxiv.org/abs/2601.14253) | [Project Page](https://motion3-to-4.github.io/) | [Code](https://github.com/Inception3D/Motion324)
 
 ## Abstract
 
-We present Motion 3-to-4, a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video and an optional 3D reference mesh. While recent advances have significantly improved 2D, video, and 3D content generation, 4D synthesis remains difficult due to limited training data and the inherent ambiguity of recovering geometry and motion from a monocular viewpoint. Motion 3-to-4 addresses these challenges by decomposing 4D synthesis into static 3D shape generation and motion reconstruction. Using a canonical reference mesh, our model learns a compact motion latent representation and predicts per-frame vertex trajectories to recover complete, temporally coherent geometry. A scalable frame-wise transformer further enables robustness to varying sequence lengths. Evaluations on both standard benchmarks and a new dataset with accurate ground-truth geometry show that Motion 3-to-4 delivers superior fidelity and spatial consistency compared to prior work. Project page is available at https://motion3-to-4.github.io/.
+Motion 3-to-4 is a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video and an optional 3D reference mesh. It addresses challenges in 4D synthesis by decomposing the task into static 3D shape generation and motion reconstruction. Using a canonical reference mesh, the model learns a compact motion latent representation and predicts per-frame vertex trajectories to recover complete, temporally coherent geometry. A scalable frame-wise transformer further enables robustness to varying sequence lengths.
 
 ## Quick Start
 
+### Installation
+
 ```bash
 git clone https://github.com/Inception3D/Motion324.git
 cd Motion324
@@ -21,11 +28,22 @@ pip install -r requirements.txt
 # (Optional) Install Hunyuan3D-2.0 modules
 cd scripts/hy3dgen/texgen/custom_rasterizer && python3 setup.py install && cd ../../../..
 cd scripts/hy3dgen/texgen/differentiable_renderer && python3 setup.py install && cd ../../../..
+```
+
+### Inference
+
+Download the pre-trained checkpoints and place them in `experiments/checkpoints/`.
 
+**Reconstruct 4D from an existing mesh and video:**
+
+```bash
 chmod +x ./scripts/4D_from_existing.sh
 ./scripts/4D_from_existing.sh ./examples/chili.glb ./examples/chili.mp4 ./examples/output
+```
 
-# Hunyuan needed
+**Generate 4D animation from a single video input (requires Hunyuan):**
+
+```bash
 chmod +x ./scripts/4D_from_video.sh
 ./scripts/4D_from_video.sh ./examples/tiger.mp4
 ```