Ankit Phogat — accepted AI-conference papers

2026

ShapeAR: Generating Editable Shape Layers via Autoregressive Diffusion

Souymodip Chakraborty, Ankur Singh, Amit Vikram Singh, Vineet Batra, Ankit Phogat

CVPR 2026

We present ShapeAR, a novel autoregressive latent diffusion framework that decomposes raster images into editable, artist-like vector shape layers. Unlike conventional raster-to-SVG methods that rely on boundary tracing or joint path optimisation, ShapeAR generates non-overlapping RGBA shape layers

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2026

VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation

Tarun Gehlaut, Difan Liu, Charu Bansal, Krutik Malani, Souymodip Chakraborty, Ankit Phogat +2

CVPR 2026

Recent vision-language model (VLM)-based approaches have achieved impressive results on image vectorization tasks. However, they are typically evaluated on synthetic benchmarks, where clean SVGs are rasterized at high resolution and then re-vectorized. As a result, these methods generalize poorly to

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