license: mit
task_categories:
- text-classification
language:
- en
tags:
- CVPR
- AI
- CV
CVPR 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2023 Open Access website (https://openaccess.thecvf.com/CVPR2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2023). For researchers who are interested in doing analysis of CVPR 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2024 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "GFPose: Learning 3D Human Pose Prior With Gradient Fields",
"authors": "Hai Ci, Mingdong Wu, Wentao Zhu, Xiaoxuan Ma, Hao Dong, Fangwei Zhong, Yizhou Wang",
"abstract": "Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D human pose to match a given task specification. During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework. Despite the simplicity, GFPose demonstrates great potential in several downstream tasks. Our experiments empirically show that 1) as a multi-hypothesis pose estimator, GFPose outperforms existing SOTAs by 20% on Human3.6M dataset. 2) as a single-hypothesis pose estimator, GFPose achieves comparable results to deterministic SOTAs, even with a vanilla backbone. 3) GFPose is able to produce diverse and realistic samples in pose denoising, completion and generation tasks.",
"pdf": "https://openaccess.thecvf.com/content/CVPR2023/papers/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.pdf",
"supp": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ci_GFPose_Learning_3D_CVPR_2023_supplemental.pdf",
"arXiv": "http://arxiv.org/abs/2212.08641",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html",
"detail_url": "https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html",
"tags": "CVPR 2023"
}
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