To address the obstacles, we fully exploit crowd features for reconstructing groups of people from a monocular image.
To address the obstacles, our key-idea is to employ physics as denoising guidance in the reverse diffusion process to reconstruct physically plausible human motion from a modeled pose probability distribution.
Point cloud registration is a task to estimate the rigid transformation between two unaligned scans, which plays an important role in many computer vision applications.
Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods.
Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region-query pairs, which further explores the high-level reasoning similarity.
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics.
Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP).
Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud.
Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem.
Ranked #1 on Unsupervised 3D Human Pose Estimation on Human3.6M (PA-MPJPE metric)
The results reveal that (1) tail items get more emphasis in hyperbolic space than that in Euclidean space, but there is still ample room for improvement; (2) head items receive modest attention in hyperbolic space, which could be considerably improved; (3) and nonetheless, the hyperbolic models show more competitive performance than Euclidean models.
Image signal processing (ISP) is crucial for camera imaging, and neural networks (NN) solutions are extensively deployed for daytime scenes.
The event camera is a bio-vision inspired camera with high dynamic range, high response speed, and low power consumption, recently attracting extensive attention for its use in vast vision tasks.
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminative at semantic and instance levels, followed by a separate step of point grouping for proposing object instances.
Ranked #6 on 3D Instance Segmentation on ScanNet(v2)
DualPoseNet stacks two parallel pose decoders on top of a shared pose encoder, where the implicit decoder predicts object poses with a working mechanism different from that of the explicit one; they thus impose complementary supervision on the training of pose encoder.
Ranked #4 on 6D Pose Estimation using RGBD on REAL275
How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing.
no code implementations • 31 Dec 2020 • Egor Ershov, Alex Savchik, Ilya Semenkov, Nikola Banić, Karlo Koscević, Marko Subašić, Alexander Belokopytov, Zhihao LI, Arseniy Terekhin, Daria Senshina, Artem Nikonorov, Yanlin Qian, Marco Buzzelli, Riccardo Riva, Simone Bianco, Raimondo Schettini, Sven Lončarić, Dmitry Nikolaev
The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.
1 code implementation • 8 May 2020 • Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte, Michael S. Brown, Yue Cao, Zhilu Zhang, WangMeng Zuo, Xiaoling Zhang, Jiye Liu, Wendong Chen, Changyuan Wen, Meng Liu, Shuailin Lv, Yunchao Zhang, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Xiyu Yu, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Songhyun Yu, Bumjun Park, Jechang Jeong, Shuai Liu, Ziyao Zong, Nan Nan, Chenghua Li, Zengli Yang, Long Bao, Shuangquan Wang, Dongwoon Bai, Jungwon Lee, Youngjung Kim, Kyeongha Rho, Changyeop Shin, Sungho Kim, Pengliang Tang, Yiyun Zhao, Yuqian Zhou, Yuchen Fan, Thomas Huang, Zhihao LI, Nisarg A. Shah, Wei Liu, Qiong Yan, Yuzhi Zhao, Marcin Możejko, Tomasz Latkowski, Lukasz Treszczotko, Michał Szafraniuk, Krzysztof Trojanowski, Yanhong Wu, Pablo Navarrete Michelini, Fengshuo Hu, Yunhua Lu, Sujin Kim, Wonjin Kim, Jaayeon Lee, Jang-Hwan Choi, Magauiya Zhussip, Azamat Khassenov, Jong Hyun Kim, Hwechul Cho, Priya Kansal, Sabari Nathan, Zhangyu Ye, Xiwen Lu, Yaqi Wu, Jiangxin Yang, Yanlong Cao, Siliang Tang, Yanpeng Cao, Matteo Maggioni, Ioannis Marras, Thomas Tanay, Gregory Slabaugh, Youliang Yan, Myungjoo Kang, Han-Soo Choi, Kyungmin Song, Shusong Xu, Xiaomu Lu, Tingniao Wang, Chunxia Lei, Bin Liu, Rajat Gupta, Vineet Kumar
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking.
Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of train- ing driving dataset is limited (2) Lack of accident explanation ability when driving models don’t work as expected.
Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of training driving dataset is limited (2) Lack of accident explanation ability when driving models don't work as expected.
Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions.
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.
In this paper, we address the problem of searching action proposals in unconstrained video clips.