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In this work, we present a novel framework built to simplify 3D assetgeneration for amateur users. To enable interactive generation, our methodsupports a variety of input modalities that can be easily provided by a human,including images, text, partially observed shapes and combinations of these,further allowing to adjust the strength of each input. At the core of ourapproach is an encoder-decoder, compressing 3D shapes into a compact latentrepresentation, upon which a diffusion model is learned. To enable a variety ofmulti-modal inputs, we employ task-specific encoders with dropout followed by across-attention mechanism. Due to its flexibility, our model naturally supportsa variety of tasks, outperforming prior works on shape completion, image-based3D reconstruction, and text-to-3D. Most interestingly, our model can combineall these tasks into one swiss-army-knife tool, enabling the user to performshape generation using incomplete shapes, images, and textual descriptions atthe same time, providing the relative weights for each input and facilitatinginteractivity. Despite our approach being shape-only, we further show anefficient method to texture the generated shape using large-scale text-to-imagemodels.

SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation