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Accidental Turntables: Learning 3D Pose by Watching Objects Turn
Zezhou Cheng, Matheus Gadelha, Subhransu Maji
We propose a technique for learning single-view 3D object pose estimation models by utilizing a new source of data -- in-the-wild videos where objects turn. Such videos are prevale…
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation
Gopal Sharma, Kangxue Yin, Subhransu Maji +3
We propose to utilize self-supervised techniques in the 2D domain for fine-grained 3D shape segmentation tasks. This is inspired by the observation that view-based surface represen…
Cross-Modal 3D Shape Generation and Manipulation
Zezhou Cheng, Menglei Chai, Jian Ren +5
Creating and editing the shape and color of 3D objects require tremendous human effort and expertise. Compared to direct manipulation in 3D interfaces, 2D interactions such as sket…
Improving Few-Shot Part Segmentation using Coarse Supervision
Oindrila Saha, Zezhou Cheng, Subhransu Maji
A significant bottleneck in training deep networks for part segmentation is the cost of obtaining detailed annotations. We propose a framework to exploit coarse labels such as figu…
How well does CLIP understand texture?
Chenyun Wu, Subhransu Maji
We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on vario…