Update app.py

app.py

@@ -141,19 +141,46 @@ def process_3d(input_image, num_steps=50, cfg_scale=7, grid_res=384, seed=42, si
 
 # gradio UI
 
-_TITLE = '''PartPacker: Efficient Part-level 3D Object Generation via Dual Volume Packing'''
+_TITLE = '''🎨 Image to 3D Model - Bring Your Images to Life!'''
 
 _DESCRIPTION = '''
-<div>
-<a style="display:inline-block" href="https://research.nvidia.com/labs/dir/partpacker/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
-<a style="display:inline-block; margin-left: .5em" href="https://github.com/NVlabs/PartPacker"><img src='https://img.shields.io/github/stars/NVlabs/PartPacker?style=social'/></a>
+<div style="text-align: center; margin-bottom: 20px;">
+    <h3 style="color: #2e7d32;">✨ Transform 2D Images into Stunning 3D Models with One Click ✨</h3>
 </div>
 
-* Each part is visualized with a random color, and can be separated in the GLB file.
-* If the output is not satisfactory, please try different random seeds!
+### 🚀 Key Features:
+- **Smart Recognition**: Automatically identifies objects in images and generates corresponding 3D models
+- **Part Separation**: Generated 3D models are automatically decomposed into multiple parts, each displayed in different colors
+- **Background Removal**: Automatically removes image backgrounds to ensure only the main object is modeled
+- **Universal Format**: Outputs standard GLB format, compatible with various 3D software
+
+### 📖 How to Use:
+1. **Upload Image**: Click the "Upload Image" area on the left to upload your picture (supports JPG, PNG, etc.)
+2. **Adjust Settings** (Optional):
+   - Higher inference steps = better quality but slower (default 50 recommended)
+   - If unsatisfied with results, try different random seeds
+3. **Click Generate**: Click the "Generate 3D Model" button and wait about 1-2 minutes
+4. **View Results**: The 3D model will appear on the right, drag with mouse to rotate and view
+
+### 💡 Tips for Best Results:
+- Clear subjects with simple backgrounds work best
+- Front-facing or 45-degree angle photos recommended
+- If results aren't ideal, try adjusting the random seed and regenerating
+- Check the example images below to see optimal input types
+
+### 🎯 Use Cases:
+- **Product Display**: Convert product images to 3D models for e-commerce
+- **Creative Design**: Quickly obtain 3D prototypes for design reference
+- **Game Development**: Generate initial 3D models for game assets
+- **Educational Demos**: Convert flat diagrams to 3D for better spatial understanding
 '''
 
-block = gr.Blocks(title=_TITLE).queue()
+block = gr.Blocks(title=_TITLE, css="""
+    .container { max-width: 1200px; margin: auto; padding: 20px; }
+    h1 { text-align: center; color: #1976d2; }
+    .examples-gallery { margin-top: 30px; }
+""").queue()
+
 with block:
     with gr.Row():
         with gr.Column():
@@ -164,32 +191,104 @@ with block:
         with gr.Column(scale=1):
             with gr.Row():
                 # input image
-                input_image = gr.Image(label="Input Image", type="filepath") # use file_path and load manually
-                seg_image = gr.Image(label="Segmentation Result", type="numpy", interactive=False, image_mode="RGBA")
-            with gr.Accordion("Settings", open=True):
+                input_image = gr.Image(
+                    label="📷 Upload Image", 
+                    type="filepath",
+                    elem_classes="input-image"
+                )
+                seg_image = gr.Image(
+                    label="🔍 Processed Image", 
+                    type="numpy", 
+                    interactive=False, 
+                    image_mode="RGBA"
+                )
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False):
+                gr.Markdown("""
+                ### Parameter Guide:
+                - **Inference Steps**: More steps = higher quality but longer processing time
+                - **CFG Scale**: Controls generation accuracy, higher values stay closer to original
+                - **Grid Resolution**: 3D model detail level, higher = more detailed
+                - **Random Seed**: Same seed produces same results, useful for reproducing effects
+                - **Simplify Mesh**: Reduces model face count for lightweight applications
+                """)
                 # inference steps
-                num_steps = gr.Slider(label="Inference steps", minimum=1, maximum=100, step=1, value=50)
+                num_steps = gr.Slider(
+                    label="Inference Steps", 
+                    minimum=1, 
+                    maximum=100, 
+                    step=1, 
+                    value=50,
+                    info="Recommended: 30-70"
+                )
                 # cfg scale
-                cfg_scale = gr.Slider(label="CFG scale", minimum=2, maximum=10, step=0.1, value=7.0)
+                cfg_scale = gr.Slider(
+                    label="CFG Scale", 
+                    minimum=2, 
+                    maximum=10, 
+                    step=0.1, 
+                    value=7.0,
+                    info="Recommended: 6-8"
+                )
                 # grid resolution
-                input_grid_res = gr.Slider(label="Grid resolution", minimum=256, maximum=512, step=1, value=384)
+                input_grid_res = gr.Slider(
+                    label="Grid Resolution", 
+                    minimum=256, 
+                    maximum=512, 
+                    step=1, 
+                    value=384,
+                    info="Recommended: 384"
+                )
                 # random seed
                 with gr.Row():
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                    randomize_seed = gr.Checkbox(
+                        label="Randomize Seed", 
+                        value=True,
+                        info="Use different seed each time"
+                    )
+                    seed = gr.Slider(
+                        label="Seed Value", 
+                        minimum=0, 
+                        maximum=MAX_SEED, 
+                        step=1, 
+                        value=0
+                    )
                 # simplify mesh
                 with gr.Row():
-                    simplify_mesh = gr.Checkbox(label="Simplify mesh", value=False)
-                    target_num_faces = gr.Slider(label="Face number", minimum=10000, maximum=1000000, step=1000, value=100000)
-                # gen button
-                button_gen = gr.Button("Generate")
+                    simplify_mesh = gr.Checkbox(
+                        label="Simplify Mesh", 
+                        value=False,
+                        info="Reduce model complexity"
+                    )
+                    target_num_faces = gr.Slider(
+                        label="Target Face Count", 
+                        minimum=10000, 
+                        maximum=1000000, 
+                        step=1000, 
+                        value=100000,
+                        info="Lower count = simpler model"
+                    )
+                
+            # gen button
+            button_gen = gr.Button("🎯 Generate 3D Model", variant="primary", size="lg")
 
         with gr.Column(scale=1):
             # glb file
-            output_model = gr.Model3D(label="Geometry", height=512)
+            output_model = gr.Model3D(
+                label="🎭 3D Model Preview", 
+                height=512,
+                elem_classes="model-viewer"
+            )
+            gr.Markdown("""
+            ### 📌 Controls:
+            - 🖱️ **Left Click & Drag**: Rotate model
+            - 🖱️ **Right Click & Drag**: Pan view
+            - 🖱️ **Scroll Wheel**: Zoom in/out
+            - 📥 Click top-right corner to download GLB file
+            """)
 
-            
     with gr.Row():
+        gr.Markdown("### 🖼️ Example Images (Click to Try):")
         gr.Examples(
             examples=[
                 ["examples/rabbit.png"],
@@ -202,11 +301,24 @@ with block:
                 ["examples/swivelchair.png"],
                 ["examples/warhammer.png"],
             ],
-            fn=process_image,  # still need to click button_gen to get the 3d
+            fn=process_image,
             inputs=[input_image],
             outputs=[seg_image],
             cache_examples=False,
+            elem_classes="examples-gallery"
         )
+    
+    gr.Markdown("""
+    ---
+    ### ⚠️ Important Notes:
+    - Generation takes 1-2 minutes, please be patient
+    - Best results with clear, prominent subjects
+    - Generated models may need further optimization in professional 3D software
+    - Each colored section represents an independent 3D part
+    
+    ### 🤝 Technical Support:
+    Powered by NVIDIA PartPacker technology. For issues, please refer to the [official documentation](https://research.nvidia.com/labs/dir/partpacker/)
+    """)
 
     button_gen.click(
         process_image, inputs=[input_image], outputs=[seg_image]