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When creating 3D content, highly specialized skills are generally needed todesign and generate models of objects and other assets by hand. We address thisproblem through high-quality 3D asset retrieval from multi-modal inputs,including 2D sketches, images and text. We use CLIP as it provides a bridge tohigher-level latent features. We use these features to perform a multi-modalityfusion to address the lack of artistic control that affects common data-drivenapproaches. Our approach allows for multi-modal conditional feature-drivenretrieval through a 3D asset database, by utilizing a combination of inputlatent embeddings. We explore the effects of different combinations of featureembeddings across different input types and weighting methods.

Zero-Shot Multi-Modal Artist-Controlled Retrieval and Exploration of 3D Object Sets