Conditional Motion In-Betweening (CMIB)
Official implementation of paper: Conditional Motion In-betweeening.
Paper(arXiv) | Project Page | YouTube
| in-betweening | pose-conditioned |
|---|---|
 |
 |
| walk | jump | dance |
|---|---|---|
 |
 |
 |
Environments
This repo is tested on following environment:
- Ubuntu 20.04
- Python >= 3.7
- PyTorch == 1.10.1
- Cuda V11.3.109
Install
-
Follow
LAFAN1dataset's installation guide. You need to install git lfs first before cloning the dataset repo. -
Run LAFAN1's
evaluate.pyto unzip and validate it. (Installnumpyfirst if you don't have it)$ pip install numpy $ python ubisoft-laforge-animation-dataset/evaluate.py
With this, you will have unpacked LAFAN dataset under
ubisoft-laforge-animation-datasetfolder. -
Install appropriate
pytorchversion depending on your device(CPU/GPU), then install packages listed inrequirements.txt. .
Trained Weights
You can download trained weights from here.
Train from Scratch
Trining script is trainer.py.
python trainer.py \
--processed_data_dir="processed_data_80/" \
--window=90 \
--batch_size=32 \
--epochs=5000 \
--device=0 \
--entity=cmib_exp \
--exp_name="cmib_80" \
--save_interval=50 \
--learning_rate=0.0001 \
--loss_cond_weight=1.5 \
--loss_pos_weight=0.05 \
--loss_rot_weight=2.0 \
--from_idx=9 \
--target_idx=88 \
--interpolation='slerp'
Inference
You can use run_cmib.py for inference. Please refer to help page of run_cmib.py for more details.
python run_cmib.py --help
Reference
- LAFAN1 Dataset
@article{harvey2020robust, author = {Félix G. Harvey and Mike Yurick and Derek Nowrouzezahrai and Christopher Pal}, title = {Robust Motion In-Betweening}, booktitle = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH)}, publisher = {ACM}, volume = {39}, number = {4}, year = {2020} }
Citation
@misc{kim2022conditional,
title={Conditional Motion In-betweening},
author={Jihoon Kim and Taehyun Byun and Seungyoun Shin and Jungdam Won and Sungjoon Choi},
year={2022},
eprint={2202.04307},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
241 May 07, 2022
59 Dec 16, 2022
15 Nov 18, 2022
157 Nov 21, 2022
55 Sep 13, 2022
39 Dec 08, 2022
86 Dec 20, 2022
105 Dec 17, 2022
110 Dec 20, 2022
3 Oct 26, 2022
12 Oct 07, 2022
25 Nov 09, 2022
161 Jan 03, 2023
530 Jan 05, 2023
2 Nov 05, 2021
1 Jan 18, 2022
17 Aug 20, 2022
0 Feb 17, 2022
1 Nov 22, 2021
100 Jan 01, 2023