20 papers with code • 2 benchmarks • 2 datasets
Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects.
We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.
In this work, we develop a framework to fuse both the single-view feature tracks and multi-view detected part locations to significantly improve the detection, localization and reconstruction of moving vehicles, even in the presence of strong occlusions.
In this article, we propose a deep reinforcement learning method to estimate the muscle excitations in simulated biomechanical systems.
Conventional SLAM techniques strongly rely on scene rigidity to solve data association, ignoring dynamic parts of the scene.
Deep Learning based Virtual Point Tracking for Real-Time Target-less Dynamic Displacement Measurement in Railway Applications
To tackle this issue, we propose virtual point tracking for real-time target-less dynamic displacement measurement, incorporating deep learning techniques and domain knowledge.
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.