Coupling Top-down and Bottom-up Methods for 3D Human Pose and Shape Estimation from Monocular Image Sequences

TL;DR

This paper introduces a combined top-down and bottom-up approach for estimating 3D human pose and shape from monocular video sequences, improving robustness and accuracy in challenging scenarios.

Contribution

It presents a novel integration of predictive cues with learned regression models and active adaptation for 3D human pose and shape estimation from monocular images.

Findings

System is fully automated and robust to noise.

Able to recover from tracking failures quickly.

Evaluated on extensive monocular human motion sequences.

Abstract

Until recently Intelligence, Surveillance, and Reconnaissance (ISR) focused on acquiring behavioral information of the targets and their activities. Continuous evolution of intelligence being gathered of the human centric activities has put increased focus on the humans, especially inferring their innate characteristics - size, shapes and physiology. These bio-signatures extracted from the surveillance sensors can be used to deduce age, ethnicity, gender and actions, and further characterize human actions in unseen scenarios. However, recovery of pose and shape of humans in such monocular videos is inherently an ill-posed problem, marked by frequent depth and view based ambiguities due to self-occlusion, foreshortening and misalignment. The likelihood function often yields a highly multimodal posterior that is difficult to propagate even using the most advanced particle filtering(PF) algorithms. Motivated by the recent success of the discriminative approaches to efficiently predict 3D poses directly from the 2D images, we present several principled approaches to integrate predictive cues using learned regression models to sustain multimodality of the posterior during tracking. Additionally, these learned priors can be actively adapted to the test data using a likelihood based feedback mechanism. Estimated 3D poses are then used to fit 3D human shape model to each frame independently for inferring anthropometric bio-signatures. The proposed system is fully automated, robust to noisy test data and has ability to swiftly recover from tracking failures even after confronting with significant errors. We evaluate the system on a large number of monocular human motion sequences.