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Recent text-to-image (T2I) generative models allow for high-quality synthesisfollowing either text instructions or visual examples. Despite theircapabilities, these models face limitations in creating new, detailed creatureswithin specific categories (e.g., virtual dog or bird species), which arevaluable in digital asset creation and biodiversity analysis. To bridge thisgap, we introduce a novel task, Virtual Creatures Generation: Given a set ofunlabeled images of the target concepts (e.g., 200 bird species), we aim totrain a T2I model capable of creating new, hybrid concepts within diversebackgrounds and contexts. We propose a new method called DreamCreature, whichidentifies and extracts the underlying sub-concepts (e.g., body parts of aspecific species) in an unsupervised manner. The T2I thus adapts to generatenovel concepts (e.g., new bird species) with faithful structures andphotorealistic appearance by seamlessly and flexibly composing learnedsub-concepts. To enhance sub-concept fidelity and disentanglement, we extendthe textual inversion technique by incorporating an additional projector andtailored attention loss regularization. Extensive experiments on twofine-grained image benchmarks demonstrate the superiority of DreamCreature overprior methods in both qualitative and quantitative evaluation. Ultimately, thelearned sub-concepts facilitate diverse creative applications, includinginnovative consumer product designs and nuanced property modifications.

DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination