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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DiffPoseNet: Direct Differentiable Camera Pose Estimation
Current deep neural network approaches for camera
pose estimation rely on scene structure for 3D motion estimation,
but this decreases the robustness and thereby makes
cross-dataset generalization difficult. In contrast, classical
approaches to structure from motion estimate 3D motion
utilizing optical flow and then compute depth. Their accuracy,
however, depends strongly on the quality of the optical
flow. To avoid this issue, direct methods have been
proposed, which separate 3D motion from depth estimation,
but compute 3D motion using only image gradients
in the form of normal flow. In this paper, we introduce
a network NFlowNet, for normal flow estimation which is
used to enforce robust and direct constraints. In particular,
normal flow is used to estimate relative camera pose based
on the cheirality (depth positivity) constraint. We achieve
this by formulating the optimization problem as a differentiable
cheirality layer, which allows for end-to-end learning
of camera pose. We perform extensive qualitative and quantitative
evaluation of the proposed DiffPoseNet’s sensitivity
to noise and its generalization across datasets. We compare
our approach to existing state-of-the-art methods on KITTI,
TartanAir, and TUM-RGBD datasets.
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- Award ID(s):
- 2020624
- NSF-PAR ID:
- 10376891
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2163-6648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
