PartComposer: Composing Part-Level Concepts from Single-Image Examples

Junyu Liu, R. Kenny Jones, Daniel Ritchie

Keywords: Visual Concepts, Concept Learning, Diffusion Model Personalization

TL;DR: Part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components.

Abstract: We present a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between denoised latents and structured concept codes via a concept predictor, enabling direct regulation on concept disentanglement and re-composition supervision. Our method achieves strong disentanglement and controllable composition, outperforming subject and part-level baselines when mixing concepts from the same, or different, object categories.

Submission Number: 28