311 citations · 979 across the 36 of their papers we have counts for
11 papers · 1 filter
Self-supervised Learning of Motion Capture
Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer +1
Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection match…
SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images
Moos Hueting, Pradyumna Reddy, Vladimir Kim +3
Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc. Although heavily researc…
Material Editing Using a Physically Based Rendering Network
Guilin Liu, Duygu Ceylan, Ersin Yumer +2
The ability to edit materials of objects in images is desirable by many content creators. However, this is an extremely challenging task as it requires to disentangle intrinsic phy…
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
Chuhang Zou, Ersin Yumer, Jimei Yang +2
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sen…
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
Xiao Yang, Ersin Yumer, Paul Asente +3
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a p…
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri +3
We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance pre…
GRASS: Generative Recursive Autoencoders for Shape Structures
Jun Li, Kai Xu, Siddhartha Chaudhuri +3
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively character…
Learning Hierarchical Shape Segmentation and Labeling from Online Repositories
Li Yi, Leonidas Guibas, Aaron Hertzmann +3
We propose a method for converting geometric shapes into hierarchically segmented parts with part labels. Our key idea is to train category-specific models from the scene graphs an…
Neural Face Editing with Intrinsic Image Disentangling
Zhixin Shu, Ersin Yumer, Sunil Hadap +3
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and…
Learning to Predict Indoor Illumination from a Single Image
Marc-André Gardner, Kalyan Sunkavalli, Ersin Yumer +4
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previo…
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
Eunbyung Park, Jimei Yang, Ersin Yumer +2
We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Instead of taking a 'blank slate' approach, we first explicitly infer…