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import gradio as gr
import pandas as pd
# Static data
STATIC_DATA = [
["VLM", "w/o WM", "–", "RGB", "72B", 50.27, 6.24],
["Image Gen.", "PathDreamer [36]", "Viewpoint", "RGB-D; Sem; Pano", "0.69B", 56.99, 5.28],
["Image Gen.", "SE3DS [11]", "Viewpoint", "RGB-D; Pano", "1.1B", 57.53, 5.29],
["Video Gen.", "NWM [25]", "Trajectory", "RGB", "1B", 57.35, 5.68],
["Video Gen.", "SVD [6]", "Image", "RGB", "1.5B", 57.71, 5.29],
["Video Gen.", "LTX-Video [5]", "Text", "RGB", "2B", 56.08, 5.37],
["Video Gen.", "Hunyuan [4]", "Text", "RGB", "13B", 57.71, 5.21],
["Video Gen.", "Wan2.1 [23]", "Text", "RGB", "14B", 58.26, 5.24],
["Video Gen.", "Cosmos [1]", "Text", "RGB", "2B", 52.27, 5.898],
["Video Gen.", "Runway", "Text", "–", "–", "–", "–"],
["Video Gen. Post-Train", "SVD† [6]", "Action", "RGB; Pano", "1.5B", 60.98, 5.02],
["Video Gen. Post-Train", "LTX† [5]", "Action", "RGB; Pano", "2B", 57.53, 5.49],
["Video Gen. Post-Train", "WAN2.1† [23]", "Action", "RGB; Pano", "14B", "XXX", "XXX"],
["Video Gen. Post-Train", "Cosmos† [1]", "Action", "RGB; Pano", "2B", 60.25, 5.08],
]
COLUMNS = ["Model Type", "Method", "Control Type", "Input Type", "#Param.", "Acc. ↑", "Mean Traj. ↓"]
def create_leaderboard():
df = pd.DataFrame(STATIC_DATA, columns=COLUMNS)
return df
# Create the Gradio interface
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>")
gr.Markdown("""
**Performance comparison across vision-language models, image generation, and video generation models.**
📊 **Metrics:** Acc. ↑ (Accuracy - higher is better) | Mean Traj. ↓ (Mean Trajectory error - lower is better)
""")
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() |