Target-Bench:

Can World Models Achieve Mapless Path Planning with Semantic Targets?

🎯 Project Page • 📄 Paper • 🤗 Dataset

[TL;DR] Target-Bench is the first benchmark and dataset for evaluating video world models (WMs) on mapless robotic path planning for semantic targets.

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TODO 📋

• Fine-tune code release (Scheduled 05 Dec)
• Benchmark code release
• Dataset release
• Paper release
• Website launch

Installation

1. Clone the repository

git clone https://github.com/TUM-AVS/target-bench.git
cd target-bench

2. Environment Setup

Ensure you have miniconda installed.

You can set up all environments at once or individually. For a quick start with VGGT:

# Install VGGT environment
bash set_env.sh vggt

For other options (installing all environments or specific ones like SpaTracker/ViPE), please refer to docs/env.md.

3. Dataset Download

Download the benchmark_data (scenarios) and wm_videos (generated videos) into the dataset/ directory:

cd dataset

# Download Benchmark scenarios
huggingface-cli download target-bench/benchmark_data --repo-type dataset --local-dir Benchmark --local-dir-use-symlinks False

# Download World Model generated videos
huggingface-cli download target-bench/wm_videos --repo-type dataset --local-dir wm_videos --local-dir-use-symlinks False

cd ..

Now, the project directory structure should look like this:

target-bench/
├── assets/                  # Images and project assets
├── dataset/                 # Benchmark data and generated videos
│   ├── Benchmark/           # Benchmark scenarios
│   └── wm_videos/           # Videos generated by world models
├── evaluation/              # Evaluation scripts and configs
├── models/                  # Source code for evaluated models
│   ├── spatracker/
│   ├── vggt/
│   └── vipe/
└── pipelines/               # World decoders adapted for each model
    ├── spatracker/
    ├── vggt/
    └── vipe/

Usage

Run a quick evaluation with 3 scenes using VGGT as the spatial-temporal tool:

conda activate vggt
cd evaluation
python target_eval_vggt.py -n 3 

Then you should be able to see the evaluation results and visualizations in the evaluation_results folder:

Citation

@article{wang2025target,
  title={Target-Bench: Can World Models Achieve Mapless Path Planning with Semantic Targets?},
  author={Wang, Dingrui and Ye, Hongyuan and Liang, Zhihao and Sun, Zhexiao and Lu, Zhaowei and Zhang, Yuchen and Zhao, Yuyu and Gao, Yuan and Seegert, Marvin and Sch{\"a}fer, Finn and others},
  journal={arXiv preprint arXiv:2511.17792},
  year={2025}
}

Credits

This project builds upon the following open-source works:

• VGGT
• ViPE
• SpatialTrackerV2

Please refer to their respective directories for detailed credits and license information.