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

• URL: http://arxiv.org/abs/2506.03004v1
• Date: Tue, 03 Jun 2025 15:43:28 GMT
• Title: PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
• Authors: Junyu Liu, R. Kenny Jones, Daniel Ritchie,
• Abstract summary: PartComposer is a framework for part-level concept learning from single-image examples.<n>We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity.
• Score: 21.521762036031618
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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