Search Results for author:
Found 7 papers, 2 papers with code
Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields
Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.
PANeRF: Pseudo-view Augmentation for Improved Neural Radiance Fields Based on Few-shot Inputs
To overcome this challenge, we propose pseudo-view augmentation of NeRF, a scheme that expands a sufficient amount of data by considering the geometry of few-shot inputs.
Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations
We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths.
Pose estimator and tracker using temporal flow maps for limbs
In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses.
Ranked #5 on
Pose Tracking
on PoseTrack2018
3D Human Pose Estimation with Relational Networks
In this paper, we propose a novel 3D human pose estimation algorithm from a single image based on neural networks.
Music Source Separation Using Stacked Hourglass Networks
In this paper, we propose a simple yet effective method for multiple music source separation using convolutional neural networks.
Sound Audio and Speech Processing
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied.
Ranked #310 on
3D Human Pose Estimation
on Human3.6M
