Abstract We present Layer3D, a novel 3D neural representation that models objects as collections of decomposable neural implicit primitives. These primitives enable the generation of layered images with consistent correspondences across viewpoints, establishing a flexible framework for multiview vector graphics decomposition. Integrated into a text‐to‐3D pipeline via Score Distillation Sampling (SDS), Layer3D learns to generate primitives with diverse shape topologies while preserving structural coherence. To ensure front‐to‐back ordering required for 2D flat graphics, our method incorporates front‐to‐back rendering and shape regularization constraints. Experimental results demonstrate that Layer3D consistently produces meaningful, topology‐diverse layers across multiple views, thereby facilitating intuitive and effective layer‐based vector editing.
Guan et al. (Tue,) studied this question.
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