2 citations · 4 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing
Purvi Goel, Loudon Cohen, James Guesman +3
Reconstructing object geometry and material from multiple views typically requires optimization. Differentiable path tracing is an appealing framework as it can reproduce complex a…