Training a 3D human keypoint detector from point clouds in a supervised
manner requires large volumes of high quality labels. While it is relatively
easy to capture large amounts of human point clouds, annotating 3D keypoints is
expensive, subjective, error prone and especially difficult for long-tail cases
(pedestrians with rare poses, scooterists, etc.). In this work, we propose
GC-KPL - Geometry Consistency inspired Key Point Leaning, an approach for
learning 3D human joint locations from point clouds without human labels. We
achieve this by our novel unsupervised loss formulations that account for the
structure and movement of the human body. We show that by training on a large
training set from Waymo Open Dataset without any human annotated keypoints, we
are able to achieve reasonable performance as compared to the fully supervised
approach. Further, the backbone benefits from the unsupervised training and is
useful in downstream fewshot learning of keypoints, where fine-tuning on only
10 percent of the labeled training data gives comparable performance to
fine-tuning on the entire set. We demonstrated that GC-KPL outperforms by a
large margin over SoTA when trained on entire dataset and efficiently leverages
large volumes of unlabeled data.

提出了一种无监督学习的方法 GC-KPL 来学习 3D 人体关键点定位，并且在未标注的数据集上取得了相对合理的性能，同时 GC-KPL 在少样本学习以及利用大量未标注数据方面得到了有效应用。