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We are witnessing rapid progress in automatically generating and manipulating3D assets due to the availability of pretrained text-image diffusion models.However, time-consuming optimization procedures are required for synthesizingeach sample, hindering their potential for democratizing 3D content creation.Conversely, 3D diffusion models now train on million-scale 3D datasets,yielding high-quality text-conditional 3D samples within seconds. In this work,we present SPiC-E - a neural network that adds structural guidance to 3Ddiffusion models, extending their usage beyond text-conditional generation. Atits core, our framework introduces a cross-entity attention mechanism thatallows for multiple entities (in particular, paired input and guidance 3Dshapes) to interact via their internal representations within the denoisingnetwork. We utilize this mechanism for learning task-specific structural priorsin 3D diffusion models from auxiliary guidance shapes. We show that ourapproach supports a variety of applications, including 3D stylization, semanticshape editing and text-conditional abstraction-to-3D, which transformsprimitive-based abstractions into highly-expressive shapes. Extensiveexperiments demonstrate that SPiC-E achieves SOTA performance over these taskswhile often being considerably faster than alternative methods. Importantly,this is accomplished without tailoring our approach for any specific task.

SPiC-E : Structural Priors in 3D Diffusion Models using Cross-Entity Attention