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arXiv · 2506.03004

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

Junyu Liu·R. Kenny Jones·Daniel Ritchie
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Abstract

We present PartComposer: 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.

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Junyu Liu, R. Kenny Jones, Daniel Ritchie. 2025-06-03. PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples. https://doi.org/10.1145/3757377.3763947

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