3D Human Mesh Estimation from Virtual Markers (2303.11726v4)

Abstract: Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://github.com/ShirleyMaxx/VirtualMarker

• The paper presents a novel approach using virtual markers to capture detailed body shapes beyond traditional skeleton-based methods.
• It leverages large-scale motion capture data to generate 64 landmark keypoints, enabling accurate mesh reconstruction via simple interpolation.
• Empirical results demonstrate improved performance across three benchmark datasets, notably on the diverse SURREAL dataset.

3D Human Mesh Estimation from Virtual Markers

This paper presents a novel approach to 3D human mesh estimation from images through the introduction of an intermediary representation termed "virtual markers." The principal aim is to overcome the limitations of traditional skeleton-based methods, which, while effective in volumetric 3D pose estimation, tend to lose important body shape information. Traditional motion capture systems counteract this limitation by utilizing dense physical markers on the body surface to capture realistic mesh data. However, these systems are constrained to controlled environments and cannot be utilized with arbitrary images captured in uncontrolled settings, often referred to as "wild images."

The authors propose an innovative solution by leveraging large-scale motion capture data to generate 64 landmark keypoints on the body surface in a generative manner, simulating the effect of these physical markers. These "virtual markers" are designed to be accurately detectable in wild images and are capable of reconstructing complete meshes with realistic shapes through simple interpolation techniques. This novel approach addresses the challenges of incorporating body shape information while maintaining the ability to operate effectively in uncontrolled imaging situations.

Empirical evaluation is a major component of the work, with the proposed method undergoing rigorous testing against state-of-the-art techniques across three benchmark datasets. The results indicate a consistent improvement in performance, most notably on the SURREAL dataset which includes a wide variety of body shapes. This is particularly significant given the diversity of body types and poses contained within this dataset, underscoring the method's robustness and adaptability.

The paper implies substantial practical implications for fields requiring precise human body modeling from 2D images, such as computer vision applications in entertainment, health, and sports. Additionally, the introduction of virtual markers paves the way for further exploration and enhancement of marker-less motion capture and 3D reconstruction techniques.

Theoretically, the strategy presents an intriguing perspective on the utilization of generative methods for deriving intermediate representations within the 3D mesh estimation process. Future research could extend the methodology by investigating different sets of virtual markers tailored for specific body types or explore more sophisticated algorithms for keypoint detection to improve accuracy and computation efficiency.

This research exemplifies the potential advancements that can be realized by bridging modern machine learning techniques with traditional approaches in computer vision, offering a promising pathway for both current applications and future innovations in 3D human modeling.