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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This content will become publicly available on November 6, 2024
Rearrangement Planning for General Part Assembly
Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system, we tackle the task of determining the precise poses of the parts in the target assembly, which we term “rearrangement planning". We present General Part Assembly Transformer (GPAT), a transformer-based model architecture that accurately predicts part poses by inferring how each part shape corresponds to the target shape. Our experiments on both 3D CAD models and real-world scans demonstrate GPAT’s generalization abilities to novel and diverse target and part shapes.
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- Award ID(s):
- 2037101
- NSF-PAR ID:
- 10483038
- Editor(s):
- Tan, Jie; Toussaint, Marc; Darvish, Kourosh
- Publisher / Repository:
- Proceedings of Machine Learning Research, MLResearchPress, https://proceedings.mlr.press/v229/li23a.html
- Date Published:
- Journal Name:
- Proceedings of the 7th Conference on Robot Learning
- Volume:
- 229
- ISSN:
- 2640-3498
- Page Range / eLocation ID:
- 127-143
- Subject(s) / Keyword(s):
- ["robotic assembly","pose estimation","3D perception"]
- Format(s):
- Medium: X
- Location:
- Atlanta, GA
- Sponsoring Org:
- National Science Foundation
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