17 citations · 30 across the 8 of their papers we have counts for
8 papers
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…
2D Neural Fields with Learned Discontinuities
Chenxi Liu, Siqi Wang, Matthew Fisher +2
Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural com…
One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Arman Maesumi, Dylan Hu, Krishi Saripalli +4
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different typ…
Learning Continuous 3D Words for Text-to-Image Generation
Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix +4
Current controls over diffusion models (e.g., through text or ControlNet) for image generation fall short in recognizing abstract, continuous attributes like illumination direction…
Segmentation-Based Parametric Painting
Manuel Ladron de Guevara, Matthew Fisher, Aaron Hertzmann
We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation. To process larg…
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Arman Maesumi, Paul Guerrero, Vladimir G. Kim +4
Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can,…