Search Results for author:
Found 15 papers, 8 papers with code
Learning Task-Specific Generalized Convolutions in the Permutohedral Lattice
When adding our network layer to state-of-the-art networks for optical flow and semantic segmentation, boundary artifacts are removed and the accuracy is improved.
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Semantic Video CNNs through Representation Warping
A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to-end training.
Towards Accurate Markerless Human Shape and Pose Estimation over Time
Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios.
A Generative Model of People in Clothing
We present the first image-based generative model of people in clothing for the full body.
Unite the People: Closing the Loop Between 3D and 2D Human Representations
With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
3D human pose and shape estimation
Monocular 3D Human Pose Estimation
Video Propagation Networks
We propose a 'Video Propagation Network' that processes video frames in an adaptive manner.
Ranked #72 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation
Our results show a simple pixel-wise decision, without any context or prior knowledge, is sufficient to provide a strong baseline on IIW.
Efficient 2D and 3D Facade Segmentation using Auto-Context
This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades.
Superpixel Convolutional Networks using Bilateral Inceptions
We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image.
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
The ability to learn more general forms of high-dimensional filters can be used in several diverse applications.
Permutohedral Lattice CNNs
This paper presents a convolutional layer that is able to process sparse input features.
Efficient Nonlinear Markov Models for Human Motion
The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods.
The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion.
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
We address the challenging task of decoupling material properties from lighting properties given a single image.
