Update for Step 1

app.py

@@ -243,43 +243,15 @@ def make3d(images):
     return mesh_fpath, mesh_glb_fpath
 
 
-_HEADER_ = '''
-<h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>
-
-**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.
-
-Code: <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a>. Techenical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.
-
-❗️❗️❗️**Important Notes:**
-- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>.
+_HEADER_ = """
+## Step 1: Generate the 3D Mesh
+For this step, we use <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>InstantMesh</a>, an open-source model for **fast** feedforward 3D mesh generation from a single image.
+During this step, you need to upload an image of what you want to generate a 3D Model from.
+## 💡 Tips
+-  If there's a background, ✅ Remove background.
 - The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
-'''
-
-_CITE_ = r"""
-If InstantMesh is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>Github Repo</a>. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/InstantMesh?style=social)](https://github.com/TencentARC/InstantMesh)
----
-📝 **Citation**
-
-If you find our work useful for your research or applications, please cite using this bibtex:
-```bibtex
-@article{xu2024instantmesh,
-  title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models},
-  author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying},
-  journal={arXiv preprint arXiv:2404.07191},
-  year={2024}
-}
-```
-
-📋 **License**
-
-Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/LICENSE) for details.
-
-📧 **Contact**
-
-If you have any questions, feel free to open a discussion or contact us at <b>bluestyle928@gmail.com</b>.
 """
 
-
 with gr.Blocks() as demo:
     gr.Markdown(_HEADER_)
     with gr.Row(variant="panel"):
@@ -368,8 +340,6 @@ with gr.Blocks() as demo:
             with gr.Row():
                 gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
 
-    gr.Markdown(_CITE_)
-
     mv_images = gr.State()
 
     submit.click(fn=check_input_image, inputs=[input_image]).success(