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We present Paint Neural Stroke Field (PaintNeSF), a novel technique togenerate stylized images of a 3D scene at arbitrary novel views from multi-view2D images. Different from existing methods which apply stylization to trainedneural radiance fields at the voxel level, our approach draws inspiration fromimage-to-painting methods, simulating the progressive painting process of humanartwork with vector strokes. We develop a palette of stylized 3D strokes frombasic primitives and splines, and consider the 3D scene stylization task as amulti-view reconstruction process based on these 3D stroke primitives. Insteadof directly searching for the parameters of these 3D strokes, which would betoo costly, we introduce a differentiable renderer that allows optimizingstroke parameters using gradient descent, and propose a training scheme toalleviate the vanishing gradient issue. The extensive evaluation demonstratesthat our approach effectively synthesizes 3D scenes with significant geometricand aesthetic stylization while maintaining a consistent appearance acrossdifferent views. Our method can be further integrated with style loss andimage-text contrastive models to extend its applications, including colortransfer and text-driven 3D scene drawing.

PaintNeSF: Artistic Creation of Stylized Scenes with Vectorized 3D Strokes