From an image of a person in action, we can easily guess the 3D motion of the person in the immediate past and future. This is because we have a mental model of 3D human dynamics that we have acquired from observing visual sequences of humans in motion. We present a framework that ...
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3D deformable model on an individual video sequence, using mask and optical flow supervision. BANMo (Yang et al.,2022) further extends the pipeline and optimizes over a few video sequences of the same object instance, with the help of a pretrained DensePose (Neverova et al.,2020) model. ...
Nellie is a comprehensive automated pipeline for studying the structure and intracellular dynamics of diverse organelles that offers accurate segmentation, tracking and feature extraction on both 2D and 3D data. Austin E. Y. T. Lefebvre ,Gabriel Sturm ...
Acquiring subcellular-level three-dimensional (3D) tissue structures efficiently without damaging the tissue remains challenging in histopathology. Here, the authors integrate holotomography with deep learning to generate 3D virtual H&E staining images from label-free thick cancer tissues, and apply this...
3D Scanning of the Human Body_Hometrica Consulting A Survey on 3D Modeling of Human Faces for Face Recognition 3D simulation of charge motion in tumbling mills by the discrete element method 3D Video Loops from Asynchronous Input Computer Vision From Surfaces to 3D Objects Development of a New ...
即直接动觉示教 (Kinematics Teaching),遥操作示教(Teleoperation)和视频演示示教(Learning from Video)，并...
Human Action Recognition (HAR) is a fundamental challenge that smart surveillance systems must overcome. With the rising affordability of capturing human a
Recognizing human actions in video sequences, known as Human Action Recognition (HAR), is a challenging task in pattern recognition. While Convolutional Neural Networks (ConvNets) have shown remarkable success in image recognition, they are not always di
Fast and stable locomotion of legged robots involves demanding and contradictory requirements, in particular rapid control frequency as well as an accurate dynamics model. Benefiting from universal approximation ability and offline optimization of neural networks, reinforcement learning has been used to solve...