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Found 5 papers, 1 papers with code
PhaseMP: Robust 3D Pose Estimation via Phase-conditioned Human Motion Prior
We present a novel motion prior, called PhaseMP, modeling a probability distribution on pose transitions conditioned by a frequency domain feature extracted from a periodic autoencoder.
Normalized Human Pose Features for Human Action Video Alignment
We present a novel approach for extracting human pose features from human action videos.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
Deep Video-Based Performance Cloning
After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances.
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications.
