TRB: A Novel Triplet Representation for Understanding 2D Human Body

10/25/2019 ∙ by Haodong Duan, et al. ∙ 10

Human pose and shape are two important components of 2D human body. However, how to efficiently represent both of them in images is still an open question. In this paper, we propose the Triplet Representation for Body (TRB) – a compact 2D human body representation, with skeleton keypoints capturing human pose information and contour keypoints containing human shape information. TRB not only preserves the flexibility of skeleton keypoint representation, but also contains rich pose and human shape information. Therefore, it promises broader application areas, such as human shape editing and conditional image generation. We further introduce the challenging problem of TRB estimation, where joint learning of human pose and shape is required. We construct several large-scale TRB estimation datasets, based on popular 2D pose datasets: LSP, MPII, COCO. To effectively solve TRB estimation, we propose a two-branch network (TRB-net) with three novel techniques, namely X-structure (Xs), Directional Convolution (DC) and Pairwise Mapping (PM), to enforce multi-level message passing for joint feature learning. We evaluate our proposed TRB-net and several leading approaches on our proposed TRB datasets, and demonstrate the superiority of our method through extensive evaluations.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 4

page 6

page 8

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

A comprehensive 2D human body representation should capture both human pose and shape information. Such representation is promising for applications beyond plain keypoint localization, such as graphics and human-computer interaction. However, how to establish such 2D body representation is still an open problem. Current mainstream 2D human body representations are not able to simultaneously capture both information. Skeleton keypoint based representation [andriluka20142d, johnson2010clustered, lin2014microsoft] well captures human poses. However, such representation loses the 2D human shape information which is essential for human body understanding. Pixel-wise human parsing representations [liang2015human, chen2014detect, gong2017look] contain 2D human shape cues. However, such kinds of representations lack accurate keypoint localization information, since all pixels in one part share the same semantic label. Meanwhile, they are inflexible to manipulate and costly to label. This paper aims at discovering a new representation for more comprehensive understanding of the human body. To this end, a novel Triplet Representation for Body (TRB) is introduced. It consists of skeleton and contour keypoint representations, capturing both accurate pose localization and rich semantic human shape information simultaneously, while preserving its flexibility and simplicity.

Since there exists no dataset to quantitatively evaluate TRB estimation, we propose several challenging TRB datasets based on three pose estimation datasets (LSP 

[johnson2010clustered], MPII [andriluka20142d] and COCO [lin2014microsoft]). We quantitatively evaluate the performance of several state-of-the-art 2D skeleton-based keypoint detectors on the proposed TRB datasets. Our experiments indicate that they are not able to effectively solve the more challenging TRB estimation tasks, which require the approaches to not only understand the concept of human pose and human shape simultaneously, but also exploit the underlying relationship between them.

For effective representation learning, we design a two-branch multi-task framework called TRB-Net, which jointly solves skeleton keypoint estimation and contour keypoint estimation. These two tasks are closely related and will promote each other. Therefore, we design a message passing block to enable information exchange. The message received from the other branch will act as guidance for the current branch to produce finer estimation results. Since feature maps from the two branches have different patterns, spatial feature transformation is necessary for feature alignment and more effective message passing scheme. Therefore, we propose a task-specific directional convolution operator to exploit the inside-out and outside-in spatial relationship between skeleton and contour feature maps. To prevent inconsistent predictions for skeleton and contour branch, we explicitly enforce pairwise mapping constraints. With these techniques, we boost the TRB estimation performance beyond state-of-the-arts. Error is reduced by 13.3% and 15.1% for skeleton and contour keypoint estimation respectively (Sec. LABEL:ablation_part).

Our contributions are three-fold: We propose the novel Triplet Representation for Body (TRB), which embodies both human pose and shape information. We apply TRB to the conditional image generation task, and show its effectiveness in handling pose/shape guided image generation and human shape editing. We introduce a challenging TRB estimation task, establish a benchmark and evaluate various mainstream pose estimation approaches in the context of TRB estimation. We design the TRB-net which jointly learns the skeleton and contour keypoint representation. Three techniques are proposed for effective message passing and feature learning. Extensive experiments show the effectiveness of our proposed methods.

2 Related Work