Semantic-Flow

[ICLR 2024] This is the official implementation of our paper "Semantic Flow: Learning Semantic Fields of Dynamic Scenes from Monocular Videos".

Semantic Flow: Learning Semantic Fields of Dynamic Scenes from Monocular Videos
Fengrui Tian, Yueqi Duan, Angtian Wang, jianfei Guo, Shaoyi Du,
in ICLR 2024

paper / pdf

Environment Setup

The code is tested with

• Ubuntu 16.04
• Anaconda 3
• Python 3.8.12
• CUDA 11.1
• 3090 GPUs

To get started, please create the conda environment semflow by running

conda create --name semflow python=3.8
conda activate semflow

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install imageio==2.19.2 pyhocon==0.3.60  pyparsing==2.4.7 configargparse==1.5.3 tensorboard==2.13.0 ipdb==0.13.13 imgviz==1.7.2 imageio--ffmpeg==0.4.8 
pip install mmcv-full==1.7.1

Then install MMAction2 v0.24.1 manually.

git clone https://github.com/open-mmlab/mmaction2.git
cd mmaction2
git checkout v0.24.1
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without re-installation.

Install other dependencies.

pip install tqdm Pillow==9.1.1

Finally, clone the Semantic Flow project:

git clone https://github.com/tianfr/Semantic-Flow.git
cd Semantic-Flow

Dataset

Our Semantic Nvidia Dataset could be accessed from this link.

After downloading the data run the following command to unzip the data

unzip data_semantic.zip

Backbone Checkpoints

Download the SlowOnly pretrained model from MMAction2 website.

mkdir checkpoints
wget -P checkpoints/ https://download.openmmlab.com/mmaction/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb_20200912-3f9ce182.pth

Training

Environment Initialization

export PYTHONPATH=.

All the training procedure is conducted on GPU 0 by default.

Multiple scenes

You can train a model from scratch by running:

chmod +x run_sh/full_label_w_config.sh
run_sh/full_label_w_config.sh 0 semflow_conf/exp/multiscenes/Balloon1_Balloon2_w_consistency_lrdecay8k.conf

Generalization

Use the checkpoints by multiple scene training and test the model on the novel scenes:

chmod +x run_sh/scene_adaptation.sh
run_sh/scene_adaptation.sh 0 2000 path_to_multiscene_ckpt  semflow_conf/exp/generation/Umbrella.conf

Tracking

Test the model on tracking task:

chmod +x run_sh/tracking_w_config.sh
run_sh/tracking_w_config.sh 0 semflow_conf/exp/tracking/Balloon2_75_track.conf

Completion

Test the generalization ability on completion task:

chmod +x run_sh/completion_w_config.sh
run_sh/completion_w_config.sh 0 Semantic-Flow/semflow_conf/exp/completion/Balloon2.conf

License

This work is licensed under MIT License. See LICENSE for details.

If you find this code useful for your research, please consider citing the following paper:

@inproceedings{24iclr/tian_semflow,
    author    = {Tian, Fengrui and Duan, Yueqi and Wang, Angtian and Guo, Jianfei and Du, Shaoyi},
    title     = {{Semantic Flow}: Learning Semantic fields of Dynamic Scenes from Monocular Videos},
    booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
    month     = {May},
    year      = {2024}
}

Acknowledgement

Our code is built upon MonoNeRF, NeRF, NeRF-pytorch, NSFF, DynamicNeRF and pixelNeRF.

Contact

If you have any questions, please feel free to contact Fengrui Tian.