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Recovering multi-person 3D poses and shapes with absolute scales from a single RGB image is a challenging task due to the inherent depth and scale ambiguity from a single view. Current works on 3D pose and shape estimation tend to mainly focus on the estimation of the 3D joint locations relative to the root joint , usually defined as the one closest to the shape centroid, in case of humans defined as the pelvis joint. In this paper, we build upon an existing multi-person 3D mesh predictor network, ROMP, to create Absolute-ROMP. By adding absolute root joint localization in the camera coordinate frame, we are able to estimate multi-person 3D poses and shapes with absolute scales from a single RGB image. Such a single-shot approach allows the system to better learn and reason about the inter-person depth relationship, thus improving multi-person 3D estimation. In addition to this end to end network, we also train a CNN and transformer hybrid network, called TransFocal, to predict the f ocal length of the image’s camera. Absolute-ROMP estimates the 3D mesh coordinates of all persons in the image and their root joint locations normalized by the focal point. We then use TransFocal to obtain focal length and get absolute depth information of all joints in the camera coordinate frame. We evaluate Absolute-ROMP on the root joint localization and root-relative 3D pose estimation tasks on publicly available multi-person 3D pose datasets. We evaluate TransFocal on dataset created from the Pano360 dataset and both are applicable to in-the-wild images and videos, due to real time performance.
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TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking
We consider the task of 3D pose estimation and tracking of multiple people seen in an arbitrary number of camera feeds. We propose TesseTrack, a novel top-down approach that simultaneously reasons about multiple individuals’ 3D body joint reconstructions and associations in space and time in a single end-to-end learnable framework. At the core of our approach is a novel spatio-temporal formulation that operates in a common voxelized feature space aggregated from single- or multiple camera views. After a person detection step, a 4D CNN produces short-term persons pecific representations which are then linked across time by a differentiable matcher. The linked descriptions are then merged and deconvolved into 3D poses. This joint spatio-temporal formulation contrasts with previous piecewise strategies that treat 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as independent sub-problems that are error-prone when solved in isolation. Furthermore, unlike previous methods, TesseTrack is robust to changes in the number of camera views and achieves very good results even if a single view is available at inference time. Quantitative evaluation of 3D pose reconstruction accuracy on standard benchmarks shows significant improvements over the state of the art. Evaluation of multi-person articulated 3D pose tracking in our novel evaluation framework demonstrates the superiority of TesseTrack over strong baselines.
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- PAR ID:
- 10292809
- Date Published:
- Journal Name:
- International Conference on Computer Vision and Pattern Recognition (CVPR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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One of the grand challenges in computer vision is to recover 3D poses and shapes of multiple human bodies with absolute scales from a single RGB image. The challenge stems from the inherent depth and scale ambiguity from a single view. The state of the art on 3D human pose and shape estimation mainly focuses on estimating the 3D joint locations relative to the root joint, defined as the pelvis joint. In this paper, a novel approach called Absolute-ROMP is proposed, which builds upon a one-stage multi-person 3D mesh predictor network, ROMP, to estimate multi-person 3D poses and shapes, but with absolute scales from a single RGB image. To achieve this, we introduce absolute root joint localization in the camera coordinate frame, which enables the estimation of 3D mesh coordinates of all persons in the image and their root joint locations normalized by the focal point. Moreover, a CNN and transformer hybrid network, called TransFocal, is proposed to predict the focal length of the image’s camera. This enables Absolute-ROMP to obtain absolute depth information of all joints in the camera coordinate frame, further improving the accuracy of our proposed method. The Absolute-ROMP is evaluated on the root joint localization and root-relative 3D pose estimation tasks on publicly available multi-person 3D pose datasets, and TransFocal is evaluated on a dataset created from the Pano360 dataset. Our proposed approach achieves state-of-the-art results on these tasks, outperforming existing methods or has competitive performance. Due to its real-time performance, our method is applicable to in-the-wild images and videos.more » « less
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Accepted Manuscript1.0
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