1 citations · 2 across the 4 of their papers we have counts for
5 papers
VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation
Tarun Gehlaut, Difan Liu, Charu Bansal +5
Recent vision-language model (VLM)-based approaches have achieved impressive results on image vectorization tasks. However, they are typically evaluated on synthetic benchmarks, wh…
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…
Learning Subject-Aware Cropping by Outpainting Professional Photos
James Hong, Lu Yuan, Michaël Gharbi +2
How to frame (or crop) a photo often depends on the image subject and its context; e.g., a human portrait. Recent works have defined the subject-aware image cropping task as a nuan…
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…