Search Results for author:
Found 11 papers, 5 papers with code
FORCE: Dataset and Method for Intuitive Physics Guided Human-object Interaction
Our key insight is that human motion is dictated by the interrelation between the force exerted by the human and the perceived resistance.
Uncertainty Estimation in Instance Segmentation with Star-convex Shapes
Two distinct clustering approaches are evaluated which compute spatial and fractional certainty per instance employing samples by the Monte-Carlo Dropout or Deep Ensemble technique.
Motion In-Betweening with Phase Manifolds
This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder.
QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
Most existing methods for motion tracking avoid environment interaction apart from foot-floor contact due to their complex dynamics and hard constraints.
Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists.
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.
Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts
Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem.
COUCH: Towards Controllable Human-Chair Interactions
In this work, we study the problem of synthesizing scene interactions conditioned on different contact positions on the object.
Machine Learning State-of-the-Art with Uncertainties
With the availability of data, hardware, software ecosystem and relevant skill sets, the machine learning community is undergoing a rapid development with new architectures and approaches appearing at high frequency every year.
Bubble identification from images with machine learning methods
An automated and reliable processing of bubbly flow images is highly needed to analyse large data sets of comprehensive experimental series.
Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases
In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases.
