Target-Bench

Can Video World Models Achieve Mapless Path Planning with Semantic Targets?

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Project Page | Paper | Dataset (Hugging Face) | Dataset (ModelScope)

TL;DR: Target-Bench is a benchmark and dataset for evaluating video world models on mapless robotic path planning with semantic targets.

TODO 📋

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

News

• [2026.07] 🚀 Target-Bench is using VGGT-Omega as the default evaluation backbone.
• [2026.06] 🎉 Target-Bench is accepted by ECCV 2026 🥳! See you September in Malmö 🇸🇪
• [2026.01] 🔥 We release the training code for fine-tuning world models.
• [2025.11] We release the paper, dataset, benchmark code, and project website.

Contents

• Installation
• Data Setup
• Option A: Hugging Face
• Option B: ModelScope
• Checkpoint Setup
• Evaluation
• Fine-tune
• Citation

Installation

Clone the repository:

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

Ensure you have miniconda installed, then create the evaluation environment:

bash set_env.sh install
conda activate target-bench-eval

This creates the target-bench-eval conda environment, installs requirements.txt, and registers the vendored VGGT/VGGT-Omega packages. The target-bench-eval environment supports all three trajectory backbones. See docs/env.md for environment variables, checkpoint paths, and verification.

We use VGGT-Omega as the default trajectory backbone because it gives stronger reconstruction quality than VGGT and DA3 in our evaluation framework.

Data Setup

Target-Bench evaluation needs two archives:

• benchmark_data.tar: benchmark scenarios, metadata, and trajectories
• wm_videos.tar: generated world-model videos

After extraction, the expected layout is:

target-bench/
└── dataset/
    ├── Benchmark/
    └── wm_videos/

Option A: Hugging Face

The Hugging Face dataset contains only the two required archives: benchmark_data.tar and wm_videos.tar.

mkdir -p dataset

hf download target-bench/dataset \
  benchmark_data.tar wm_videos.tar \
  --repo-type dataset \
  --local-dir dataset

tar -xf dataset/benchmark_data.tar -C dataset
tar -xf dataset/wm_videos.tar -C dataset

Option B: ModelScope

ModelScope is sometimes faster. The ModelScope dataset also contains preprocessed_dataset.tar, but it is not required for benchmark evaluation. Download only benchmark_data.tar and wm_videos.tar:

mkdir -p dataset

modelscope download \
  --dataset parkring/target-bench \
  benchmark_data.tar wm_videos.tar \
  --local_dir dataset

tar -xf dataset/benchmark_data.tar -C dataset
tar -xf dataset/wm_videos.tar -C dataset

Checkpoint Setup

Evaluation uses VGGT-Omega by default. Download the default checkpoint into checkpoints/ before running evaluation:

mkdir -p checkpoints/vggt_omega
hf download facebook/vggt-omega \
  vggt_omega_1b_512.pt \
  --local-dir checkpoints/vggt_omega

VGGT-Omega is a gated model, so run hf auth login first if needed. To set up optional DA3 and VGGT checkpoints, see docs/checkpoints.md.

Evaluation

VGGT-Omega is the default evaluation backbone:

conda activate target-bench-eval
python evaluation/evaluate.py

For a quick smoke test:

python evaluation/evaluate.py -n 3

The same entry point can run the other supported backbones:

python evaluation/evaluate.py --backbone vggt  # da3 or vggt_omega

To evaluate only gt_videos or specific video world model:

python evaluation/evaluate.py --models gt_videos
python evaluation/evaluate.py --models sora-2

To run all supported backbones:

bash evaluation/evaluate.sh

Results are written to evaluation_results/. The main files are:

• model_summary_*.csv
• segment_model_results_*.csv
• evaluation_results_*.json

Fine-tune

The fine-tuning code is kept under DiffSynth-Studio/.

1. Fine-tune Environment

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

2. Checkpoints

mkdir -p DiffSynth-Studio/models/train/ckpts
hf download target-bench/ckpts \
  --repo-type model \
  --local-dir DiffSynth-Studio/models/train/ckpts

3. Fine-tune Dataset

hf download target-bench/finetune_dataset \
  --repo-type dataset \
  --local-dir dataset

unzip dataset/data_four_segments_121_frames.zip -d dataset
unzip dataset/data_single_segment_121_frames.zip -d dataset
unzip dataset/data_inference.zip -d dataset

4. Inference

Run inference with the released fine-tuned checkpoint:

(cd DiffSynth-Studio && python run_inference_four_segments_epoch-49_batch.py)

5. LoRA Fine-tuning

Fine-tune Wan2.2-TI2V-5B on Target-Bench scenarios:

(cd DiffSynth-Studio && bash Wan2.2-TI2V-5B_four_segments.sh)

Citation

@inproceedings{wang2025target,
  title={Target-Bench: Can Video 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},
  booktitle={European Conference on Computer Vision},
  year={2026},
  organization={Springer}
}

Credits

This project builds on the following open-source works:

• VGGT
• VGGT-Omega
• Depth Anything 3
• ViPE
• SpatialTrackerV2
• DiffSynth-Studio

Please refer to their respective repositories and licenses for details.

License

Target-Bench original code is released under the MIT License. Third-party components vendored in this repository are governed by their own licenses. In particular, VGGT-Omega is released under the FAIR Noncommercial Research License and may restrict commercial use.