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20162020
most citedMarrNet: 3D Shape Reconstruction via 2.5D Sketches

237 citations · 465 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CV20205 cited

Multi-Plane Program Induction with 3D Box Priors

Yikai Li, Jiayuan Mao, Xiuming Zhang +4

We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene.…

cs.CV2020

Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows

Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu +3

Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervis…

cs.CV201880 cited

Learning to Reconstruct Shapes from Unseen Classes

Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang +3

From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction a…

cs.CV2018

Visual Object Networks: Image Generation with Disentangled 3D Representation

Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang +4

Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an…

cs.CV2017237 cited

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

Jiajun Wu, Yifan Wang, Tianfan Xue +3

3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-…

cs.CV2016143 cited

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Tianfan Xue, Jiajun Wu, Katherine L. Bouman +1

We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a determinis…