CANVAS: Consistency-Aware Navigation via Visual Adaptive Sampling for Long-Context Text-to-SVG Generation
2026-08-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address a problem in generating complex graphics with text-based instructions, where typical methods struggle to keep the overall design consistent. They propose CANVAS, a new way to improve this by using visual feedback from temporarily rendered images to better decide each stroke in the drawing. This approach helps maintain correct layouts, geometry, and layers without needing extra training. Their tests show it works well across different models and tasks, improving the final graphic's quality.
Text-to-SVGautoregressive modelsglobal consistencyvisual feedbacktrajectory likelihoodstroke-wise navigationrenderingsamplinglayoutocclusion
Authors
Yichen Wu, Haoxuan Qu, Yihang Lou, Hossein Rahmani, Jun Liu
Abstract
Autoregressive large models have recently advanced Text-to-SVG generation from simple icons to complex, long-context graphics, yet standard autoregressive decoding often fails to maintain global consistency across geometry, layout, occlusion, and composition. We introduce CANVAS (Consistency-Aware Navigation via Visual Adaptive Sampling), a training-free, render-aware inference framework that combines power-sharpened trajectory likelihood with visual feedback from rendered futures and derives a stroke-wise navigation rule. It effectively estimates each candidate stroke's future value under a limited generation and rendering budget and adaptively allocates samples according to candidate uncertainty, decision influence, and rollout cost. Experiments across multiple autoregressive SVG backbones and complementary benchmarks demonstrate improvements in global consistency, which includes sound geometric relationships, spatial layouts, occlusion ordering, and overall composition, without additional training, demonstrating the effectiveness and generalization ability of our framework.