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Motion Estimation

38 papers with code · Computer Vision

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Greatest papers with code

Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics

12 Jun 2019tensorflow/models

We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion.

DEPTH AND CAMERA MOTION MOTION ESTIMATION

Unsupervised Learning of Depth and Ego-Motion from Video

CVPR 2017 tinghuiz/SfMLearner

We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences.

DEPTH AND CAMERA MOTION MOTION ESTIMATION POSE ESTIMATION

GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

CVPR 2018 yzcjtr/GeoNet

We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.

IMAGE RECONSTRUCTION MOTION ESTIMATION OPTICAL FLOW ESTIMATION

QuaterNet: A Quaternion-based Recurrent Model for Human Motion

16 May 2018facebookresearch/QuaterNet

Deep learning for predicting or generating 3D human pose sequences is an active research area.

3D HUMAN POSE ESTIMATION MOTION ESTIMATION

On human motion prediction using recurrent neural networks

CVPR 2017 facebookresearch/QuaterNet

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality.

MOTION ESTIMATION

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

CVPR 2019 anuragranj/ac

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.

MONOCULAR DEPTH ESTIMATION MOTION ESTIMATION MOTION SEGMENTATION OPTICAL FLOW ESTIMATION

Spatio-temporal video autoencoder with differentiable memory

19 Nov 2015viorik/ConvLSTM

At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame.

MOTION ESTIMATION OPTICAL FLOW ESTIMATION WEAKLY-SUPERVISED SEMANTIC SEGMENTATION