Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop

Model-based human pose estimation is currently approached through two different paradigms. Optimization-based methods fit a parametric body model to 2D observations in an iterative manner, leading to accurate image-model alignments, but are often slow and sensitive to the initialization. In contrast, regression-based methods, that use a deep network to directly estimate the model parameters from pixels, tend to provide reasonable, but not pixel accurate, results while requiring huge amounts of supervision. In this work, instead of investigating which approach is better, our key insight is that the two paradigms can form a strong collaboration. A reasonable, directly regressed estimate from the network can initialize the iterative optimization making the fitting faster and more accurate. Similarly, a pixel accurate fit from iterative optimization can act as strong supervision for the network. This is the core of our proposed approach SPIN (SMPL oPtimization IN the loop). The deep network initializes an iterative optimization routine that fits the body model to 2D joints within the training loop, and the fitted estimate is subsequently used to supervise the network. Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network. We demonstrate the effectiveness of our approach in different settings, where 3D ground truth is scarce, or not available, and we consistently outperform the state-of-the-art model-based pose estimation approaches by significant margins. The project website with videos, results, and code can be found at https://seas.upenn.edu/~nkolot/projects/spin.

1. Introduction

With the emergence of deep learning architectures, the dilemma between regression-based and optimization-based approaches for many computer vision problems has been more relevant than ever. Should we regress the relative camera pose, or use bundle adjustment? Is it more appropriate to regress the parameters of a face model, or fit the model to facial landmarks? These types of questions are ubiquitous within our community. Among others, 3D model-based human pose estimation has initiated similar discussions, since both optimization-based [4], [18] and regression-based approaches [15], [24], [27] have had significant success recently. However, one can argue that both paradigms have weak and strong points ( Figure 1). Based on this, in this work we advocate that instead of focusing on which paradigm is better, if we aim to push the field forward, we need to consider ways for collaboration between the two. Figure 1:

Both optimization and regression approaches have successes and failures, so this motivates our approach to build a tight collaboration between the two.