Abstract
Keypoint detection is an essential component for the object registration and alignment. However, previous works mainly focused on how to register keypoints under arbitrary rigid transformations. Differently, in this work, we reckon keypoints under an information compression scheme to represent the whole object. Based on this, we propose UKPGAN, an unsupervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints preserve the semantic information of objects and align well with human annotated part and keypoint labels. Furthermore, we show that UKPGAN can be applied to either rigid objects or non-rigid SMPL human bodies under arbitrary pose deformations. As a keypoint detector, our model is stable under both rigid and non-rigid transformations, with local reference frame estimation. Our code is available on this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2011.11974
