347 citations · 366 across the 6 of their papers we have counts for
18 papers
Fine Detailed Texture Learning for 3D Meshes with Generative Models
Aysegul Dundar, Jun Gao, Andrew Tao +1
This paper presents a method to reconstruct high-quality textured 3D models from both multi-view and single-view images. The reconstruction is posed as an adaptation problem and is…
View Generalization for Single Image Textured 3D Models
Anand Bhattad, Aysegul Dundar, Guilin Liu +2
Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view gene…
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
Guilin Liu, Rohan Taori, Ting-Chun Wang +6
Conventional CNNs for texture synthesis consist of a sequence of (de)-convolution and up/down-sampling layers, where each layer operates locally and lacks the ability to capture th…
Hierarchical Multi-Scale Attention for Semantic Segmentation
Andrew Tao, Karan Sapra, Bryan Catanzaro
Multi-scale inference is commonly used to improve the results of semantic segmentation. Multiple images scales are passed through a network and then the results are combined with a…
Panoptic-based Image Synthesis
Aysegul Dundar, Karan Sapra, Guilin Liu +2
Conditional image synthesis for generating photorealistic images serves various applications for content editing to content generation. Previous conditional image synthesis algorit…
Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos
Aysegul Dundar, Kevin J. Shih, Animesh Garg +3
Unsupervised landmark learning is the task of learning semantic keypoint-like representations without the use of expensive input keypoint-level annotations. A popular approach is t…