Search Results for author:
Found 7 papers, 3 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.
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
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.
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding.
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.
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.
