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Significant progress has recently been made in creative applications of largepre-trained models for downstream tasks in 3D vision, such as text-to-shapegeneration. This motivates our investigation of how these pre-trained modelscan be used effectively to generate 3D shapes from sketches, which has largelyremained an open challenge due to the limited sketch-shape paired datasets andthe varying level of abstraction in the sketches. We discover that conditioninga 3D generative model on the features (obtained from a frozen large pre-trainedvision model) of synthetic renderings during training enables us to effectivelygenerate 3D shapes from sketches at inference time. This suggests that thelarge pre-trained vision model features carry semantic signals that areresilient to domain shifts, i.e., allowing us to use only RGB renderings, butgeneralizing to sketches at inference time. We conduct a comprehensive set ofexperiments investigating different design factors and demonstrate theeffectiveness of our straightforward approach for generation of multiple 3Dshapes per each input sketch regardless of their level of abstraction withoutrequiring any paired datasets during training.

Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation