update

app.py

@@ -281,8 +281,13 @@ def main(args_1, args_2):
     with gr.Blocks(css=css) as demo:
         gr.Markdown(
             """
+            <div>
+            <a style="display:inline-block" href="https://nirvanalan.github.io/projects/GA/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+            <a style="display:inline-block; margin-left: .5em" href="https://github.com/NIRVANALAN/GaussianAnything"><img src='https://img.shields.io/github/stars/NIRVANALAN/GaussianAnything?style=social'/></a>
+            </div>
+
             # GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation
-            **GaussianAnything (arXiv 2024)** [[code](https://github.com/NIRVANALAN/GaussianAnything), [project page](https://nirvanalan.github.io/projects/GA/)] is a native 3D diffusion model that supports high-quality 2D Gaussians generation. 
+            **GaussianAnything is a native 3D diffusion model that supports high-quality 2D Gaussians generation. 
             It first trains a 3D VAE on **Objaverse**, which compress each 3D asset into a compact point cloud-structured latent. 
             After that, a image/text-conditioned diffusion model is trained following LDM paradigm.
             The model used in the demo adopts 3D DiT architecture and flow-matching framework, and supports single-image condition.