52 citations · 89 across the 7 of their papers we have counts for
12 papers
How to Train Your Dragon: Automatic Diffusion-Based Rigging for Characters with Diverse Topologies
Zeqi Gu, Difan Liu, Timothy Langlois +2
Recent diffusion-based methods have achieved impressive results on animating images of human subjects. However, most of that success has built on human-specific body pose represent…
Mean-Shift Distillation for Diffusion Mode Seeking
Vikas Thamizharasan, Nikitas Chatzis, Iliyan Georgiev +5
We present mean-shift distillation, a novel diffusion distillation technique that provides a provably good proxy for the gradient of the diffusion output distribution. This is deri…
NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation
Vikas Thamizharasan, Difan Liu, Matthew Fisher +3
The success of denoising diffusion models in representing rich data distributions over 2D raster images has prompted research on extending them to other data representations, such…
VecFusion: Vector Font Generation with Diffusion
Vikas Thamizharasan, Difan Liu, Shantanu Agarwal +5
We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded d…
LRM: Large Reconstruction Model for Single Image to 3D
Yicong Hong, Kai Zhang, Jiuxiang Gu +7
We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods…
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…
CSGNet: Neural Shape Parser for Constructive Solid Geometry
Gopal Sharma, Rishabh Goyal, Difan Liu +2
We present a neural architecture that takes as input a 2D or 3D shape and outputs a program that generates the shape. The instructions in our program are based on constructive soli…