NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation

no code implementations • 24 May 2024 • Vikas Thamizharasan, Difan Liu, Matthew Fisher, Nanxuan Zhao, Evangelos Kalogerakis, Michal Lukac

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 as vector graphics.

GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis

no code implementations • 26 Feb 2024 • Dmitry Petrov, Pradyumn Goyal, Vikas Thamizharasan, Vladimir G. Kim, Matheus Gadelha, Melinos Averkiou, Siddhartha Chaudhuri, Evangelos Kalogerakis

We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes.

Denoising Point cloud reconstruction +1

VecFusion: Vector Font Generation with Diffusion

no code implementations • 16 Dec 2023 • Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis

We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.

Font Generation Vector Graphics

Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing

1 code implementation • 6 Dec 2020 • Purvi Goel, Loudon Cohen, James Guesman, Vikas Thamizharasan, James Tompkin, Daniel Ritchie

In this paper, we explore how to use differentiable ray tracing to refine an initial coarse mesh and per-mesh-facet material representation.

Lighting Estimation