Awesome

<br>This repository contains the code for the SingularTrajectory model, designed to handle five different trajectory prediction benchmarks. <br>Our unified framework ensures the general dynamics of human movements across various input modalities and trajectory lengths.

1️⃣ SingularTrajectory Model 1️⃣

• A diffusion-based universal trajectory prediction framework designed to bridge the performance gap across five tasks.
• A Singular space is constructed to unify various representations of human dynamics in the associated tasks.
• An adaptive anchor and cascaded denoising process correct initial prototype paths that are placed incorrectly.
• Our model outperforms on five public benchmarks: Deterministic, Stochastic, Domain Adaptation, Momentary Observation, and Few-Shot.

Model Training

Setup

Environment <br>All models were trained and tested on Ubuntu 20.04 with Python 3.8 and PyTorch 2.0.1 with CUDA 11.7.

Dataset <br>Preprocessed ETH and UCY datasets are released in this repository. The train/validation/test splits are the same as those found in Social-GAN.

You can download the dataset by running the following script.

./script/download_datasets.sh

Train SingularTrajectory

To train our SingularTrajectory on each task using the ETH and UCY datasets simultaneously, we provide a bash script train.sh for simplified execution.

./script/train.sh -p <config_path> -t <experiment_tag> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

Examples

# Stochastic prediction task
./script/train.sh -p stochastic/singulartrajectory -t SingularTrajectory-stochastic

# Deterministic prediction task
./script/train.sh -p deterministic/singulartrajectory -t SingularTrajectory-deterministic

# Momentary observation task
./script/train.sh -p momentary/singulartrajectory -t SingularTrajectory-momentary

# Domain adaptation task
./script/train.sh -p domain/singulartrajectory -t SingularTrajectory-domain -d "A2B A2C A2D A2E B2A B2C B2D B2E C2A C2B C2D C2E D2A D2B D2C D2E E2A E2B E2C E2D" -i "0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4"

# Few-shot task
./script/train.sh -p fewshot/singulartrajectory -t SingularTrajectory-fewshot

# (Optional) Stochastic domain adaptation task
./script/train.sh -p domain-stochastic/singulartrajectory -t SingularTrajectory-domain-stochastic -d "A2B A2C A2D A2E B2A B2C B2D B2E C2A C2B C2D C2E D2A D2B D2C D2E E2A E2B E2C E2D" -i "0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4"

# (Optional) All-in-one task
./script/train.sh -p allinone/singulartrajectory -t SingularTrajectory-allinone -d "A2B A2C A2D A2E B2A B2C B2D B2E C2A C2B C2D C2E D2A D2B D2C D2E E2A E2B E2C E2D" -i "0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4"

If you want to train the model with custom hyperparameters, use trainval.py instead of the script file.

python trainval.py --cfg ./config/{task}/singulartrajectory-transformerdiffusion-{dataset}.json --tag SingularTrajectory-{task} --gpu_id 0 

Model Evaluation

Pretrained Models

We provide pretrained models in the release section. You can download all pretrained models at once by running the script. This will download the 80 SingularTrajectory models.

./script/download_pretrained_models.sh

Evaluate SingularTrajectory

To evaluate our EigenTrajectory at once, we provide a bash script test.sh for a simplified execution.

./script/test.sh -p <config_path> -t <experiment_tag> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

Examples

# Stochastic prediction task
./script/test.sh -p stochastic/singulartrajectory -t SingularTrajectory-stochastic

# Deterministic prediction task
./script/test.sh -p deterministic/singulartrajectory -t SingularTrajectory-deterministic

# Momentary observation task
./script/test.sh -p momentary/singulartrajectory -t SingularTrajectory-momentary

# Domain adaptation task
./script/test.sh -p domain/singulartrajectory -t SingularTrajectory-domain -d "A2B A2C A2D A2E B2A B2C B2D B2E C2A C2B C2D C2E D2A D2B D2C D2E E2A E2B E2C E2D" -i "0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4"

# Few-shot task
./script/test.sh -p fewshot/singulartrajectory -t SingularTrajectory-fewshot

# (Optional) Stochastic domain adaptation task
./script/test.sh -p domain-stochastic/singulartrajectory -t SingularTrajectory-domain-stochastic -d "A2B A2C A2D A2E B2A B2C B2D B2E C2A C2B C2D C2E D2A D2B D2C D2E E2A E2B E2C E2D" -i "0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4"

# (Optional) All-in-one task
./script/test.sh -p allinone/singulartrajectory -t SingularTrajectory-allinone -d "A2B A2C A2D A2E B2A B2C B2D B2E C2A C2B C2D C2E D2A D2B D2C D2E E2A E2B E2C E2D" -i "0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4"

If you want to evaluate the model individually, you can use trainval.py with custom hyperparameters.

python trainval.py --test --cfg ./config/{task}/singulartrajectory-transformerdiffusion-{dataset}.json --tag SingularTrajectory-{task} --gpu_id 0

📖 Citation

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

💬 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{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{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{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}
}

Acknowledgement

Part of our code is borrowed from EigenTrajectory and LED. We thank the authors for releasing their code and models.