Zhicheng Zhong

Yifan Chen, Zhiyu Pan, Zhicheng Zhong et al.

In this paper, we present a novel registration framework, HumanReg, that learns a non-rigid transformation between two human point clouds end-to-end. We introduce body prior into the registration process to efficiently handle this type of point cloud. Unlike most exsisting supervised registration techniques that require expensive point-wise flow annotations, HumanReg can be trained in a self-supervised manner benefiting from a set of novel loss functions. To make our model better converge on real-world data, we also propose a pretraining strategy, and a synthetic dataset (HumanSyn4D) consists of dynamic, sparse human point clouds and their auto-generated ground truth annotations. Our experiments shows that HumanReg achieves state-of-the-art performance on CAPE-512 dataset and gains a qualitative result on another more challenging real-world dataset. Furthermore, our ablation studies demonstrate the effectiveness of our synthetic dataset and novel loss functions. Our code and synthetic dataset is available at https://github.com/chenyifanthu/HumanReg.

Zhiyu Pan, Zhicheng Zhong, Wenxuan Guo et al.

Several methods have been proposed to estimate 3D human pose from multi-view images, achieving satisfactory performance on public datasets collected under relatively simple conditions. However, there are limited approaches studying extracting 3D human skeletons from multimodal inputs, such as RGB and point cloud data. To address this gap, we introduce LiCamPose, a pipeline that integrates multi-view RGB and sparse point cloud information to estimate robust 3D human poses via single frame. We demonstrate the effectiveness of the volumetric architecture in combining these modalities. Furthermore, to circumvent the need for manually labeled 3D human pose annotations, we develop a synthetic dataset generator for pretraining and design an unsupervised domain adaptation strategy to train a 3D human pose estimator without manual annotations. To validate the generalization capability of our method, LiCamPose is evaluated on four datasets, including two public datasets, one synthetic dataset, and one challenging self-collected dataset named BasketBall, covering diverse scenarios. The results demonstrate that LiCamPose exhibits great generalization performance and significant application potential. The code, generator, and datasets will be made available upon acceptance of this paper.