5 citations · 5 across the 2 of their papers we have counts for
6 papers
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
Difan Liu, Sandesh Shetty, Tobias Hinz +4
We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map. Our architecture is…
Neural Strokes: Stylized Line Drawing of 3D Shapes
Difan Liu, Matthew Fisher, Aaron Hertzmann +1
This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes,…
Neural Contours: Learning to Draw Lines from 3D Shapes
Difan Liu, Mohamed Nabail, Aaron Hertzmann +1
This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3…
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
Gopal Sharma, Difan Liu, Subhransu Maji +3
We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic…
Neural Shape Parsers for Constructive Solid Geometry
Gopal Sharma, Rishabh Goyal, Difan Liu +2
Constructive Solid Geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinde…
Deep Part Induction from Articulated Object Pairs
Li Yi, Haibin Huang, Difan Liu +3
Object functionality is often expressed through part articulation -- as when the two rigid parts of a scissor pivot against each other to perform the cutting function. Such articul…