Resolving 3D Human Pose Ambiguities with 3D Scene Constraints (1908.06963v1)

Published 20 Aug 2019 in cs.CV

Abstract: To understand and analyze human behavior, we need to capture humans moving in, and interacting with, the world. Most existing methods perform 3D human pose estimation without explicitly considering the scene. We observe however that the world constrains the body and vice-versa. To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene. Our key contribution is to exploit static 3D scene structure to better estimate human pose from monocular images. The method enforces Proximal Relationships with Object eXclusion and is called PROX. To test this, we collect a new dataset composed of 12 different 3D scenes and RGB sequences of 20 subjects moving in and interacting with the scenes. We represent human pose using the 3D human body model SMPL-X and extend SMPLify-X to estimate body pose using scene constraints. We make use of the 3D scene information by formulating two main constraints. The inter-penetration constraint penalizes intersection between the body model and the surrounding 3D scene. The contact constraint encourages specific parts of the body to be in contact with scene surfaces if they are close enough in distance and orientation. For quantitative evaluation we capture a separate dataset with 180 RGB frames in which the ground-truth body pose is estimated using a motion capture system. We show quantitatively that introducing scene constraints significantly reduces 3D joint error and vertex error. Our code and data are available for research at https://prox.is.tue.mpg.de.

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Authors (4)

Mohamed Hassan (22 papers)
Vasileios Choutas (12 papers)
Dimitrios Tzionas (35 papers)
Michael J. Black (163 papers)

Citations (260)

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Summary

Analyzing 3D Human Pose Estimation with 3D Scene Constraints

The paper "Resolving 3D Human Pose Ambiguities with 3D Scene Constraints" by Mohamed Hassan et al. addresses significant challenges faced in 3D human pose estimation, particularly the inaccuracies that arise when estimation is performed without considering the constraints imposed by the surrounding 3D scene. Traditional models often disregard the physical interactions and interference between the human body and its environment, leading to results that may appear valid from a monocular camera perspective but are inconsistent with the spatial context. The authors introduce a novel method named PROX (Proximal Relationships with Object eXclusion) that utilizes 3D scene constraints to enhance the accuracy and realism of human pose estimations from monocular RGB images.

The key contribution of this research is the integration of scene-specific constraints — specifically, inter-penetration and contact — into the pose estimation pipeline. The authors propose two primary constraints: the inter-penetration constraint, which penalizes any intersection between the body model and the 3D scene, and the contact constraint, which promotes proximity between the body and potential contact surfaces in the scene based on certain body parts' orientations and distances. By integrating these constraints, the paper demonstrates a significant reduction in 3D joint and vertex errors, thus improving the fidelity of pose predictions.

To validate their approach, the authors employ the SMPL-X model and extend SMPLify-X with these scene constraints to estimate human poses. They conduct experiments on three datasets, including their newly captured dataset, to provide a qualitative and quantitative assessment of the method’s efficacy. The quantitative evaluation demonstrates that incorporation of scene constraints yields a 24.4% reduction in mean per-joint error and a 27.6% reduction in mean vertex-to-vertex error compared to baseline methods that do not utilize scene constraints.

From a theoretical standpoint, the paper underscores the value of context-aware models in computer vision applications. Practically, this offers potential advancements in various fields requiring accurate human pose estimation, such as animation, virtual reality, and human-robot interaction. Another practical implication can be found in fields such as surveillance and safety monitoring, where the understanding of human-environment interaction is crucial.

The paper does not delve into modeling dynamic scenes, which limits its applicability in scenarios with moving objects. Additionally, exploring methods to incorporate scene occlusion reasoning could further improve robustness. Future research could investigate these areas as well as explore the integration of more advanced deep learning techniques for estimating scenes from single monocular images and refining pose estimation dynamically.

In conclusion, the application of 3D scene constraints in human pose estimation represents a significant methodological improvement, addressing critical limitations of prior approaches by marrying human pose and environmental context into a coherent estimation system. This synthesis lays the groundwork for future work on human-scene interaction modeling and posits further exploration into dynamic environments and real-time processing.

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