most citedTransformation-Grounded Image Generation Network for Novel 3D View Synthesis

37 citations · 84 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV201717 cited

Learning Dense Facial Correspondences in Unconstrained Images

Ronald Yu, Shunsuke Saito, Haoxiang Li +2

We present a minimalistic but effective neural network that computes dense facial correspondences in highly unconstrained RGB images. Our network learns a per-pixel flow and a matc…

cs.CV20176 cited

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…

cs.CV201722 cited

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…

cs.CV20172 cited

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…

cs.CV201737 cited

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…

cs.CV2016

Capturing Dynamic Textured Surfaces of Moving Targets

Ruizhe Wang, Lingyu Wei, Etienne Vouga +4

We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld…