33 citations · 70 across the 14 of their papers we have counts for
17 papers · 1 filter
Illustrator's Depth: Monocular Layer Index Prediction for Image Decomposition
Nissim Maruani, Peiying Zhang, Siddhartha Chaudhuri +6
We introduce Illustrator's Depth, a novel definition of depth that addresses a key challenge in digital content creation: decomposing flat images into editable, ordered layers. Ins…
DECOLLAGE: 3D Detailization by Controllable, Localized, and Learned Geometry Enhancement
Qimin Chen, Zhiqin Chen, Vladimir G. Kim +3
We present a 3D modeling method which enables end-users to refine or detailize 3D shapes using machine learning, expanding the capabilities of AI-assisted 3D content creation. Give…
Temporal Residual Jacobians For Rig-free Motion Transfer
Sanjeev Muralikrishnan, Niladri Shekhar Dutt, Siddhartha Chaudhuri +4
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume access to any rigging or intermediate shape k…
Learning to Infer Generative Template Programs for Visual Concepts
R. Kenny Jones, Siddhartha Chaudhuri, Daniel Ritchie
People grasp flexible visual concepts from a few examples. We explore a neurosymbolic system that learns how to infer programs that capture visual concepts in a domain-general fash…
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan +5
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes. The key ingredient of our method is a neural skeleton-based representation encoding information on b…
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
Xianghao Xu, Paul Guerrero, Matthew Fisher +2
Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes…