A pose-estimation network pretrained on macaque monkey images and fine-tuned on 1,000 human images outperformed a human-only benchmark on precision, recall, and F1, but not on AUC.
Space-Time Representation of People Based on 3D Skeletal Data: A Review
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Based on the information sources, these representations can be broadly categorized into two groups based on RGB-D information or 3D skeleton data. Recently, skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of public benchmark datasets with skeleton data, and discuss potential future research directions.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Monkey Transfer Learning Can Improve Human Pose Estimation
A pose-estimation network pretrained on macaque monkey images and fine-tuned on 1,000 human images outperformed a human-only benchmark on precision, recall, and F1, but not on AUC.