PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision

Kehong Gong*, Bingbing Li*, Jianfeng Zhang*, Tao Wang*, Jing Huang, Bi Mi, Jiashi Feng, Xinchao Wang

CVPR 2022 (Oral Presentation, arxiv)

Logo

Framework

Pose-triplet contains three components: estimator, imitator and hallucinator

The three components form dual-loop during the training process, complementing and strengthening one another. alt text

Improvement through co-evolving

Here is imitated motion of different rounds, the estimator and imitator get improved over the rounds of training, and thus the imitated motion becomes more accurate and realistic from round 1 to 3. alt text

Video demo

04806-supp.mp4

Comparasion

Here we compared our results with two recent works Yu et al. and Hu et al.

Installation

TBD.

Data Preparation

TBD.

Inference

We provide an inference code here. Please follow the instruction and download the pretrained model for inference on images/videos.

Training (training files will be released soon…)

TBD.

Citation

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

@inproceedings{gong2022posetriplet,
  title      = {PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision},
  author     = {Gong, Kehong and Li, Bingbing and Zhang, Jianfeng and Wang, Tao and Huang, Jing and Mi, Michael Bi and Feng, Jiashi and Wang, Xinchao},
  booktitle  = {CVPR},
  year       = {2022}
}

GitHub

View Github