Search Results for author:
Found 7 papers, 2 papers with code
InterAct: Capture and Modelling of Realistic, Expressive and Interactive Activities between Two Persons in Daily Scenarios
We address the problem of accurate capture and expressive modelling of interactive behaviors happening between two persons in daily scenarios.
Taming Diffusion Probabilistic Models for Character Control
We present a novel character control framework that effectively utilizes motion diffusion probabilistic models to generate high-quality and diverse character animations, responding in real-time to a variety of dynamic user-supplied control signals.
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.
