Putting People in their Place: Monocular Regression of 3D People in Depth

Sun, Yu; Liu, Wu; Bao, Qing; Fu, Yili; Mei, Tao; Black, Michael J.

doi:10.48550/arxiv.2112.08274

Cited by 2 publications

(4 citation statements)

References 23 publications

(60 reference statements)

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“…3D pose estimation from monocular inputs [14][15][16][17][18][19][20][21][22] presents an ill-posed problem, as multiple 3D predictions can correspond to the same 2D projection. Multi-view approaches have been developed to alleviate such projective ambiguity.…”

Section: D Human Pose Estimationmentioning

confidence: 99%

Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator

Deng,

Yao,

Shi

2023

Sensors

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Data-driven pose estimation methods often assume equal distributions between training and test data. However, in reality, this assumption does not always hold true, leading to significant performance degradation due to distribution mismatches. In this study, our objective is to enhance the cross-domain robustness of multi-view, multi-person 3D pose estimation. We tackle the domain shift challenge through three key approaches: (1) A domain adaptation component is introduced to improve estimation accuracy for specific target domains. (2) By incorporating a dropout mechanism, we train a more reliable model tailored to the target domain. (3) Transferable Parameter Learning is employed to retain crucial parameters for learning domain-invariant data. The foundation for these approaches lies in the H-divergence theory and the lottery ticket hypothesis, which are realized through adversarial training by learning domain classifiers. Our proposed methodology is evaluated using three datasets: Panoptic, Shelf, and Campus, allowing us to assess its efficacy in addressing domain shifts in multi-view, multi-person pose estimation. Both qualitative and quantitative experiments demonstrate that our algorithm performs well in two different domain shift scenarios.

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Section: D Human Pose Estimationmentioning

confidence: 99%

Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator

Deng,

Yao,

Shi

2023

Sensors

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“…Multi-person 3D mesh regression. There have been few works [12,23,61,62,70,73] that concern the multi-person 3D body mesh regression: The approaches could be categorized into two: bottom-up and top-down methods.…”

Section: Related Workmentioning

confidence: 99%

“…Bottom-up methods [23,61,62,73] perform multi-person detection and 3D mesh reconstruction simultaneously. Zhang et al [73] proposed a Body Mesh as Point (BMP) using a multi-scale 2D center map grid-level representation, which locates selective persons at the grid cell's center.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement

Cha¹,

Saqlain²,

GeonU³

et al. 2022

Preprint

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Estimating 3D poses and shapes in the form of meshes from monocular RGB images is challenging. Obviously, it is more difficult than estimating 3D poses only in the form of skeletons or heatmaps. When interacting persons are involved, the 3D mesh reconstruction becomes more challenging due to the ambiguity introduced by person-to-person occlusions. To tackle the challenges, we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics from the occlusion-robust 3D skeleton estimation and 2) Transformer-based relation-aware refinement techniques. In our pipeline, we first obtain occlusion-robust 3D skeletons for multiple persons from an RGB image. Then, we apply inverse kinematics to convert the estimated skeletons to deformable 3D mesh parameters. Finally, we apply the Transformer-based mesh refinement that refines the obtained mesh parameters considering intra-and inter-person relations of 3D meshes. Via extensive experiments, we demonstrate the effectiveness of our method, outperforming state-of-the-arts on 3DPW, MuPoTS and AGORA datasets.

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Putting People in their Place: Monocular Regression of 3D People in Depth

Abstract: Figure 1. Monocular reconstruction of multiple 3D people with coherent depth reasoning. We introduce BEV, a monocular one-stage method with an efficient new "bird's-eye-view" representation, which enables the network to explicitly reason about people in 3D.

Cited by 2 publications

References 23 publications

Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator

Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator

Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement

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