Update layout

app.py

@@ -35,6 +35,19 @@ with gr.Blocks(title="World-in-World: Building a Closed-Loop World Interface to
     gr.HTML("<h1 style='text-align: center; margin-bottom: 1rem'>🏆 World-in-World: Building a Closed-Loop World Interface to Evaluate World Models</h1>")
     
     with gr.Tabs():
+        # New Zone before Leaderboard
+        with gr.TabItem("🧑‍🏫 Instruction & Environment Feedback"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.HTML("<h3 style='text-align: center;'>Instruction:</h3>")
+                    gr.Markdown("Navigate to the Toaster in the room and be as close as possible to it.")
+                    
+                with gr.Column(scale=2):
+                    gr.HTML("<h3 style='text-align: center;'>Closed-Loop Environmental Feedback</h3>")
+                    gr.Video("/home/user/app/demo_source_data/AR/FTwan21_lora/X7HyMhZNoso/E145/A001/world_model_gen/bbox_gen_video_1.mp4", label="Left Image")
+                    gr.Image("/home/user/app/demo_source_data/AR/FTwan21_lora/X7HyMhZNoso/E145/A001/real_obs.png", label="Bird Eye View", type="pil")
+                    gr.Model3D("/home/user/app/demo_source_data/scenes_glb/5ZKStnWn8Zo.glb", label="3D Scene")
+
         with gr.TabItem("📊 Leaderboard"):
             leaderboard_table = gr.DataFrame(
                 value=create_leaderboard(),
@@ -69,4 +82,4 @@ with gr.Blocks(title="World-in-World: Building a Closed-Loop World Interface to
             """)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

app_dup.py

@@ -0,0 +1,72 @@
+import gradio as gr
+import pandas as pd
+
+# Static data - reordered columns: Method, #Param., Input Type, Control Type, Model Type, Mean Traj. ↓, Acc. ↑
+STATIC_DATA = [
+    ["w/o WM", "72B", "RGB", "–", "VLM", 6.24, 50.27],
+    ["PathDreamer [36]", "0.69B", "RGB-D; Sem; Pano", "Viewpoint", "Image Gen.", 5.28, 56.99],
+    ["SE3DS [11]", "1.1B", "RGB-D; Pano", "Viewpoint", "Image Gen.", 5.29, 57.53],
+    ["NWM [25]", "1B", "RGB", "Trajectory", "Video Gen.", 5.68, 57.35],
+    ["SVD [6]", "1.5B", "RGB", "Image", "Video Gen.", 5.29, 57.71],
+    ["LTX-Video [5]", "2B", "RGB", "Text", "Video Gen.", 5.37, 56.08],
+    ["Hunyuan [4]", "13B", "RGB", "Text", "Video Gen.", 5.21, 57.71],
+    ["Wan2.1 [23]", "14B", "RGB", "Text", "Video Gen.", 5.24, 58.26],
+    ["Cosmos [1]", "2B", "RGB", "Text", "Video Gen.", 5.898, 52.27],
+    ["Runway", "–", "–", "Text", "Video Gen.", "–", "–"],
+    ["SVD† [6]", "1.5B", "RGB; Pano", "Action", "Video Gen. Post-Train", 5.02, 60.98],
+    ["LTX† [5]", "2B", "RGB; Pano", "Action", "Video Gen. Post-Train", 5.49, 57.53],
+    ["WAN2.1† [23]", "14B", "RGB; Pano", "Action", "Video Gen. Post-Train", "XXX", "XXX"],
+    ["Cosmos† [1]", "2B", "RGB; Pano", "Action", "Video Gen. Post-Train", 5.08, 60.25],
+]
+
+COLUMNS = ["Method", "#Param.", "Input Type", "Control Type", "Model Type", "Mean Traj. ↓", "Acc. ↑"]
+
+def create_leaderboard():
+    df = pd.DataFrame(STATIC_DATA, columns=COLUMNS)
+    # Sort by accuracy in descending order (highest first), handling non-numeric values
+    df_clean = df.copy()
+    # Replace non-numeric values with -1 for sorting (so they appear at bottom)
+    df_clean['Acc. ↑'] = pd.to_numeric(df_clean['Acc. ↑'], errors='coerce').fillna(-1)
+    df_sorted = df_clean.sort_values('Acc. ↑', ascending=False)
+    # Return original df with the sorted order but original values
+    return df.iloc[df_sorted.index].reset_index(drop=True)
+
+with gr.Blocks(title="World-in-World: Building a Closed-Loop World Interface to Evaluate World Models", theme=gr.themes.Soft()) as demo:
+    gr.HTML("<h1 style='text-align: center; margin-bottom: 1rem'>🏆 World-in-World: Building a Closed-Loop World Interface to Evaluate World Models</h1>")
+    
+    with gr.Tabs():
+        with gr.TabItem("📊 Leaderboard"):
+            leaderboard_table = gr.DataFrame(
+                value=create_leaderboard(),
+                headers=COLUMNS,
+                datatype=["str", "str", "str", "str", "str", "number", "number"],
+                interactive=False,
+                wrap=True
+            )
+        
+        with gr.TabItem("📝 About"):
+            gr.Markdown("""
+            # World-in-World: Building a Closed-Loop World Interface to Evaluate World Models
+            
+            This leaderboard showcases performance metrics across different types of AI models in world modeling tasks:
+            
+            ## Model Categories
+            - **VLM**: Vision-Language Models
+            - **Image Gen.**: Image Generation Models  
+            - **Video Gen.**: Video Generation Models
+            - **Video Gen. Post-Train**: Post-training specialized Video Generation Models
+            
+            ## Metrics Explained
+            - **Acc. ↑**: Accuracy score (higher values indicate better performance)
+            - **Mean Traj. ↓**: Mean trajectory error (lower values indicate better performance)
+            
+            ## Notes
+            - † indicates post-training specialized models
+            - XXX indicates results pending/unavailable
+            - – indicates not applicable or not available
+            
+            *Results represent performance on world modeling evaluation benchmarks and may vary across different evaluation settings.*
+            """)
+
+if __name__ == "__main__":
+    demo.launch()

demo_source_data

@@ -0,0 +1 @@
+Subproject commit 444734e2db2d0dc0533f12e31185e5859117b670