Search Results for author:
Found 15 papers, 12 papers with code
MIDGET: Music Conditioned 3D Dance Generation
In this paper, we introduce a MusIc conditioned 3D Dance GEneraTion model, named MIDGET based on Dance motion Vector Quantised Variational AutoEncoder (VQ-VAE) model and Motion Generative Pre-Training (GPT) model to generate vibrant and highquality dances that match the music rhythm.
DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation
Specifically, for an initial hand-object pose estimated by a base network, we forward it to a physics simulator to evaluate its stability.
Scene-aware Human Motion Forecasting via Mutual Distance Prediction
In this paper, we tackle the problem of scene-aware 3D human motion forecasting.
TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs
Direction of arrival (DOA) estimation employing low-resolution analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing problem, particularly with the rise in popularity of large-scale arrays.
VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos
Different from their works which sparsify voxels globally with a fixed occupancy threshold, we perform the sparsification on a local feature volume along each visual ray to preserve at least one voxel per ray for more fine details.
Interacting Hand-Object Pose Estimation via Dense Mutual Attention
In contrast, we propose a novel dense mutual attention mechanism that is able to model fine-grained dependencies between the hand and the object.
Ranked #2 on
hand-object pose
on HO-3D
(using extra training data)
Contact-aware Human Motion Forecasting
In this paper, we tackle the task of scene-aware 3D human motion forecasting, which consists of predicting future human poses given a 3D scene and a past human motion.
Ranked #2 on
Human Pose Forecasting
on GTA-IM Dataset
Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction
We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history.
Generating Smooth Pose Sequences for Diverse Human Motion Prediction
Recent progress in stochastic motion prediction, i. e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse future motions and even providing control over the motion of some body parts.
Ranked #2 on
Human Pose Forecasting
on AMASS
(ADE metric)
Multi-level Motion Attention for Human Motion Prediction
Whether based on recurrent or feed-forward neural networks, existing learning based methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities.
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually Impaired
Based on Lintention, we then devise a novel panoptic segmentation model which we term Panoptic Lintention Net.
History Repeats Itself: Human Motion Prediction via Motion Attention
Human motion prediction aims to forecast future human poses given a past motion.
Can we cover navigational perception needs of the visually impaired by panoptic segmentation?
Navigational perception for visually impaired people has been substantially promoted by both classic and deep learning based segmentation methods.
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
We propose a cost volume-based neural network for depth inference from multi-view images.
Ranked #14 on
3D Reconstruction
on DTU
Learning Trajectory Dependencies for Human Motion Prediction
In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.
