Target-Bench:

Can World Models Achieve Mapless Path Planning with Semantic Targets?

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🎯 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.

TODO 📋

• Fine-tuned checkpoints release
• Fine-tune code release
• Benchmark code release
• Dataset release
• Paper release
• Website launch

News

• [2026.01] We release the training code for fine-tuning world models!
• [2025.11] We release the Paper, Dataset, and Benchmark Code!

Table of Contents

• Installation
  • Clone the repository
  • Evaluation Environment Setup
  • Dataset Download
• Evaluation
  • Quick Evaluate with VGGT
  • Evaluation Result Visualization
• Fine-tune
  • Fine-tune Environment Setup
  • Checkpoint Download
  • Prepare Inference & Fine-tune Dataset
  • Inference with Fine-tuned Checkpoint
  • Fine-tune with LoRA
• Citation

Installation

1. Clone the repository

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

2. Evaluation 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/
├── DiffSynth-Studio/        # DiffSynth-Studio for fine-tuning
├── 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/

Evaluation

Quick Evaluate with VGGT

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 

Evaluation Result Visualization

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

Fine-tune

1. Fine-tune Environment Setup

conda deactivate
conda create -n target-finetune python=3.10 -y
conda activate target-finetune
cd DiffSynth-Studio
pip install -r requirements.txt

2. Checkpoint Download

cd DiffSynth-Studio/models/train
# Download Fine-tuned Checkpoint
huggingface-cli download target-bench/ckpts --repo-type model --local-dir ckpts --local-dir-use-symlinks False
cd ../..

3. Prepare Inference & Fine-tune Dataset

huggingface-cli download target-bench/finetune_dataset --repo-type dataset --local-dir dataset --local-dir-use-symlinks False
cd dataset
# data_four_segments_121_frames.zip contains the data augmentation result
unzip data_four_segments_121_frames.zip data_single_segment_121_frames.zip data_inference.zip
cd ..

4. Inference with Fine-tuned Checkpoint

Inference with Checkpoint finetuned using data augmentation.

python run_inference_four_segments_epoch-49_batch.py

5. Fine-tune with LoRA

Fine-tune Wan2.2-TI2V-5B on 325 scenarios using data augmentation result.

bash Wan2.2-TI2V-5B_four_segments.sh

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
• DiffSynth-Studio

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