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CLIPDraw++: Text-to-Sketch Synthesis with Simple Primitives
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CLIPDraw++: Text-to-Sketch Synthesis with Simple Primitives

Nityanand Mathur, Shyam Marjit, Abhra Chaudhuri and Anjan Dutta
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, pp.6237-6246
11/06/2025

Abstract

CLIP Embeddings Concept Visualization Conferences Explainable AI Geometric Primitives Linear Transformations Mathematical models Merging Noise Optimization Optimization-based Drawing Pattern recognition Shape Sketch Synthesis Vector Graphics Vectors Visualization
With the goal of understanding the visual concepts that CLIP associates with text prompts, we show that the latent space of CLIP can be visualized solely in terms of linear transformations on simple geometric primitives like straight lines and circles. Although existing approaches achieve this by sketch-synthesis-through-optimization, they do so on the space of higher order Bézier curves, which exhibit a wastefully large set of structures that they can evolve into, as most of them are non-essential for generating meaningful sketches. We present CLIPDraw++, an algorithm that provides significantly better visualizations for CLIP text embeddings, using only simple primitive shapes like straight lines and circles. This constrains the set of possible outputs to linear transformations on these primitives, thereby exhibiting an inherently simpler mathematical form. The synthesis process of CLIPDraw++ can be tracked end-toend, with each visual concept being expressed exclusively in terms of primitives.

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