Towards Viewpoint Invariant 3D Human Pose Estimation
This addresses the problem of viewpoint variation in 3D human pose estimation for computer vision applications, representing an incremental improvement with specific gains.
The paper tackles 3D human pose estimation from single depth images by proposing a viewpoint invariant model that embeds local regions into a learned feature space, achieving state-of-the-art performance on alternate viewpoints and competitive results on frontal views.
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve this, our discriminative model embeds local regions into a learned viewpoint invariant feature space. Formulated as a multi-task learning problem, our model is able to selectively predict partial poses in the presence of noise and occlusion. Our approach leverages a convolutional and recurrent network architecture with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner. We evaluate our model on a previously published depth dataset and a newly collected human pose dataset containing 100K annotated depth images from extreme viewpoints. Experiments show that our model achieves competitive performance on frontal views while achieving state-of-the-art performance on alternate viewpoints.