activity
20162026
most citedLearning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

1.6k citations · 1.8k across the 8 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

cs.CV2018★ 80 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★ 57 cited

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.CV2018★ 12 cited

Learning Shape Priors for Single-View 3D Completion and Reconstruction

Jiajun Wu, Chengkai Zhang, Xiuming Zhang +3

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not…

cs.CV2018★ 1 cited

Seeing Tree Structure from Vibration

Tianfan Xue, Jiajun Wu, Zhoutong Zhang +3

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance…

cs.CV2018★ 51 cited

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

Xingyuan Sun, Jiajun Wu, Xiuming Zhang +5

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pix…