  Recent text-to-image (T2I) generative models allow for high-quality synthesis
following either text instructions or visual examples. Despite their
capabilities, these models face limitations in creating new, detailed creatures
within specific categories (e.g., virtual dog or bird species), which are
valuable in digital asset creation and biodiversity analysis. To bridge this
gap, we introduce a novel task, Virtual Creatures Generation: Given a set of
unlabeled images of the target concepts (e.g., 200 bird species), we aim to
train a T2I model capable of creating new, hybrid concepts within diverse
backgrounds and contexts. We propose a new method called DreamCreature, which
identifies and extracts the underlying sub-concepts (e.g., body parts of a
specific species) in an unsupervised manner. The T2I thus adapts to generate
novel concepts (e.g., new bird species) with faithful structures and
photorealistic appearance by seamlessly and flexibly composing learned
sub-concepts. To enhance sub-concept fidelity and disentanglement, we extend
the textual inversion technique by incorporating an additional projector and
tailored attention loss regularization. Extensive experiments on two
fine-grained image benchmarks demonstrate the superiority of DreamCreature over
prior methods in both qualitative and quantitative evaluation. Ultimately, the
learned sub-concepts facilitate diverse creative applications, including
innovative consumer product designs and nuanced property modifications.
