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Darsh Kaushik

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Noam Aigerman
Sujets de recherche
Apprentissage profond
Vision par ordinateur
Google Scholar
GitHub

Publications

WildSVG: Towards Reliable SVG Generation Under Real-Word Conditions
Marco Terral
Haotian Zhang
Tianyang Zhang
Meng Lin
Xiaoqing Xie
Haoran Dai
Darsh Kaushik
Pai Peng
Nicklas Scharpff
David Vázquez
Joan Rodriguez
We introduce the task of SVG extraction, which consists in translating specific visual inputs from an image into scalable vector graphics. E… (voir plus)xisting multimodal models achieve strong results when generating SVGs from clean renderings or textual descriptions, but they fall short in real-world scenarios where natural images introduce noise, clutter, and domain shifts. A central challenge in this direction is the lack of suitable benchmarks. To address this need, we introduce the WildSVG Benchmark, formed by two complementary datasets: Natural WildSVG, built from real images containing company logos paired with their SVG annotations, and Synthetic WildSVG, which blends complex SVG renderings into real scenes to simulate difficult conditions. Together, these resources provide the first foundation for systematic benchmarking SVG extraction. We benchmark state-of-the-art multimodal models and find that current approaches perform well below what is needed for reliable SVG extraction in real scenarios. Nonetheless, iterative refinement methods point to a promising path forward, and model capabilities are steadily improving
2026-02-23
arXiv (prépublication)
doi.org
arxiv.org
VectorGym: A Multitask Benchmark for SVG Code Generation, Sketching, and Editing
Juan Rodriguez
Haotian Zhang
Abhay Puri
Tianyang Zhang
Rishav Pramanik
Meng Lin
Xiaoqing Xie
Marco Terral
Darsh Kaushik
Aly Shariff
Perouz Taslakian
Spandana Gella
Sai Rajeswar
David Vázquez
Christopher Pal
Marco Pedersoli
We introduce VectorGym, a comprehensive benchmark suite for Scalable Vector Graphics (SVG) that spans generation from text and sketches, com… (voir plus)plex editing, and visual understanding. VectorGym addresses the lack of realistic, challenging benchmarks aligned with professional design workflows. Our benchmark comprises four tasks with expert human-authored annotations: the novel Sketch2SVG task (VG-Sketch); a new SVG editing dataset (VG-Edit) featuring complex, multi-step edits with higher-order primitives; Text2SVG generation (VG-Text); and SVG captioning (VG-Cap). Unlike prior benchmarks that rely on synthetic edits, VectorGym provides gold-standard human annotations that require semantic understanding and design intent. We also propose a multi-task reinforcement learning approach that jointly optimizes across all four tasks using rendering-based rewards. Our method, built on GRPO with curriculum learning, trains a Qwen3-VL 8B model that achieves state-of-the-art performance among open-source models, surpassing much larger models including Qwen3-VL 235B and matching GPT-4o. We also introduce a VLM-as-a-Judge metric for SVG generation, validated through human correlation studies. Our evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation. VectorGym is publicly available on huggingface.co/datasets/ServiceNow/VectorGym.
2026-02-21
arXiv (prépublication)
doi.org
arxiv.org