Continuous Locomotive Crowd Behavior Generation

Inhwan Bae · Junoh Lee · Hae-Gon Jeon
CVPR 2025

Project Page CVPR Paper Source Code 3D Toolkit Related Works

Generating realistic, continuous crowd behaviors with learned dynamics.


(Left) Time-varying behavior changes, (Right) Real2Sim evaluation on New York City.
More video examples are available on our project page!

Summary: A crowd emitter diffusion model and a state-switching crowd simulator for populating input scene images and generating lifelong crowd trajectories.

🏢🚶‍♂️ Crowd Behavior Generation Benchmark 🏃‍♀️🏠

• Repurposed Trajectory Datasets: A new benchmark that reuses existing real-world human trajectory datasets, adapting them for crowd trajectory generation.
• Image-Only Input: Eliminates conventional observation trajectory dependency and requires only a single input image to fully populate the scene with crowds.
• Lifelong Simulation: Generates continuous trajectories where people dynamically enter and exit the scene, replicating the ever-changing real-world crowd dynamics.
• Two-Tier Evaluation: Assesses performance on both scene-level realism (e.g., density, frequency, coverage, and population metrics) and agent-level accuracy (e.g., kinematics, DTW, diversity, and collision rate).

🚵 CrowdES Framework 🚵

• Crowd Emitter: A diffusion-based model that iteratively “emits” new agents by sampling when and where they appear on spatial layouts.
• Crowd Simulator: A state-switching system that generates continuous trajectories with agents dynamically switching behavior modes.
• Controllability & Flexibility: Users can override or customize scene-level and agent-level parameters at runtime.
• Sim2Real & Real2Sim Capability: The framework can bridge synthetic and real-world scenarios for interdisciplinary research.

🔥 Model Training

Setup

Environment
All models were trained and tested on Ubuntu 20.04 with Python 3.10 and PyTorch 2.2.2 with CUDA 12.1. You can install all dependencies via following command:

pip install -r requirements.txt

Dataset
Preprocessed ETH, UCY, SDD and EDIN datasets are released in this repository.

[!NOTE]
If you want to preprocess the datasets by yourself, please download the raw datasets and run the following command:

python utils/preprocess_dataset.py --model_config <path_to_model_config>

# Example
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_eth.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_hotel.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_univ.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_zara1.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_zara2.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_sdd.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_gcs.yaml
python utils/preprocess_dataset.py --model_config ./configs/model/CrowdES_edin.yaml

Train Crowd Emitter Model

To train the CrowdES crowd emitter model, you can use the following command:

python trainval.py --model_train emitter_pre --model_config <path_to_model_config>
python trainval.py --model_train emitter --model_config <path_to_model_config>

# Example
python trainval.py --model_train emitter_pre --model_config ./configs/model/CrowdES_eth.yaml
python trainval.py --model_train emitter --model_config ./configs/model/CrowdES_eth.yaml

Train Crowd Simulator Model

To train the CrowdES crowd simulator model, you can use the following command:

python trainval.py --model_train simulator --model_config <path_to_model_config>

# Example
python trainval.py --model_train simulator --model_config ./configs/model/CrowdES_eth.yaml

📊 Model Evaluation

Pretrained Models

We provide pretrained models in the release section.

Evaluate CrowdES

To evaluate the CrowdES model, you can use the following command:

python trainval.py --test --model_config <path_to_model_config>

# Example
python trainval.py --test --model_config ./configs/model/CrowdES_eth.yaml

🚀 Model Inference

Export Generated Trajectories

To export the generated trajectories from the CrowdES model, you can use the following command:

python trainval.py --export --model_config <path_to_model_config>

# Example
python trainval.py --export --model_config ./configs/model/CrowdES_eth.yaml

[!TIP] You can also customize the hyperparameters for exporting the generated trajectories by modifying the CrowdES/evaluate_export_generated_traj.py file. Here are the default settings:

https://github.com/InhwanBae/Crowd-Behavior-Generation/blob/97a7bc74748aeaaef8b25594005201a82c800559/CrowdES/evaluate_export_generated_traj.py#L11-L17

Run CrowdES with Custom Input

To evaluate the CrowdES model with a custom input image, you can use the following command:

python trainval.py --synthetic --model_config <path_to_model_config>

# Example
python trainval.py --synthetic --model_config ./configs/model/CrowdES_eth.yaml

[!TIP] You can also customize the hyperparameters by modifying the CrowdES/evaluate_synthetic.py file. Here are the default settings:

https://github.com/InhwanBae/Crowd-Behavior-Generation/blob/97a7bc74748aeaaef8b25594005201a82c800559/CrowdES/evaluate_synthetic_dataset.py#L9-L14

🌏 3D Visualization

To visualize the generated crowd behaviors in 3D, we provide a visualization toolkit based on the CARLA simulator. Please follow the instructions in the 3D_Visualization_Toolkit/README file to set up the environment and visualize the results.

📖 Citation

If you find this code useful for your research, please cite our trajectory prediction papers :)

🏢🚶‍♂️ CrowdES (CVPR'25) 🏃‍♀️🏠 | 💭 VLMTrajectory (TPAMI) 💭 | 💬 LMTrajectory (CVPR'24) 🗨️ | 1️⃣ SingularTrajectory (CVPR'24) 1️⃣ | 🌌 EigenTrajectory (ICCV'23) 🌌 | 🚩 Graph‑TERN (AAAI'23) 🚩 | 🧑‍🤝‍🧑 GP‑Graph (ECCV'22) 🧑‍🤝‍🧑 | 🎲 NPSN (CVPR'22) 🎲 | 🧶 DMRGCN (AAAI'21) 🧶

@inproceedings{bae2025crowdes,
  title={Continuous Locomotive Crowd Behavior Generation},
  author={Bae, Inhwan and Lee, Junoh and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2025}
}

@article{bae2025vlmtrajectory,
  title={Social Reasoning-Aware Trajectory Prediction via Multimodal Language Model},
  author={Bae, Inhwan and Lee, Junoh and Jeon, Hae-Gon},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2025}
}

@inproceedings{bae2024lmtrajectory,
  title={Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction},
  author={Bae, Inhwan and Lee, Junoh and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

@inproceedings{bae2024singulartrajectory,
  title={SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model},
  author={Bae, Inhwan and Park, Young-Jae and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

@inproceedings{bae2023eigentrajectory,
  title={EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting},
  author={Bae, Inhwan and Oh, Jean and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

@article{bae2023graphtern,
  title={A Set of Control Points Conditioned Pedestrian Trajectory Prediction},
  author={Bae, Inhwan and Jeon, Hae-Gon},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2023}
}

@inproceedings{bae2022gpgraph,
  title={Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction},
  author={Bae, Inhwan and Park, Jin-Hwi and Jeon, Hae-Gon},
  booktitle={Proceedings of the European Conference on Computer Vision},
  year={2022}
}

@inproceedings{bae2022npsn,
  title={Non-Probability Sampling Network for Stochastic Human Trajectory Prediction},
  author={Bae, Inhwan and Park, Jin-Hwi and Jeon, Hae-Gon},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

@article{bae2021dmrgcn,
  title={Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction},
  author={Bae, Inhwan and Jeon, Hae-Gon},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2021}
}