It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data

We address the problem of 3D human pose estimation from 2D input images using only weakly supervised training data. Despite showing considerable success for 2D pose estimation, the application of supervised machine learning to 3D pose estimation in real world images is currently hampered by the lack of varied training images with corresponding 3D poses. Most existing 3D pose estimation algorithms train on data that has either been collected in carefully controlled studio settings or has been generated synthetically. Instead, we take a different approach, and propose a 3D human pose estimation algorithm that only requires relative estimates of depth at training time. Such training signal, although noisy, can be easily collected from crowd annotators, and is of sufficient quality for enabling successful training and evaluation of 3D pose algorithms. Our results are competitive with fully supervised regression based approaches on the Human3.6M dataset, despite using significantly weaker training data. Our proposed algorithm opens the door to using existing widespread 2D datasets for 3D pose estimation by allowing fine-tuning with noisy relative constraints, resulting in more accurate 3D poses.Comment: BMVC 2018. Project page available at http://www.vision.caltech.edu/~mronchi/projects/RelativePos

TIC-TAC: A Framework To Learn And Evaluate Your Covariance

We study the problem of unsupervised heteroscedastic covariance estimation, where the goal is to learn the multivariate target distribution $\mathcal{N}(y, \Sigma_y | x )$ given an observation $x$. This problem is particularly challenging as $\Sigma_{y}$ varies for different samples (heteroscedastic) and no annotation for the covariance is available (unsupervised). Typically, state-of-the-art methods predict the mean $f_{\theta}(x)$ and covariance $\textrm{Cov}(f_{\theta}(x))$ of the target distribution through two neural networks trained using the negative log-likelihood. This raises two questions: (1) Does the predicted covariance truly capture the randomness of the predicted mean? (2) In the absence of ground-truth annotation, how can we quantify the performance of covariance estimation? We address (1) by deriving TIC: Taylor Induced Covariance, which captures the randomness of the multivariate $f_{\theta}(x)$ by incorporating its gradient and curvature around $x$ through the second order Taylor polynomial. Furthermore, we tackle (2) by introducing TAC: Task Agnostic Correlations, a metric which leverages conditioning of the normal distribution to evaluate the covariance. We verify the effectiveness of TIC through multiple experiments spanning synthetic (univariate, multivariate) and real-world datasets (UCI Regression, LSP, and MPII Human Pose Estimation). Our experiments show that TIC outperforms state-of-the-art in accurately learning the covariance, as quantified through TAC.Comment: 12 pages, 4 figures. Please feel free to provide feedback