Bottom-up Modeling of Repeated Elements via Single Image Analysis-by-Synthesis

• URL: http://arxiv.org/abs/2609.07939v1
• Date: Mon, 07 Sep 2026 19:53:11 GMT
• Title: Bottom-up Modeling of Repeated Elements via Single Image Analysis-by-Synthesis
• Abstract summary: We show that a single image can suffice to learn a meaningful object model in a completely bottom-up fashion.<n>Our method learns a tunable image-space prototype of the repeated elements through a reconstruction objective.
• Score: 45.330889352313825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets, curated multi-image collections, or object segmentation masks, we show that a single image can suffice to learn a meaningful object model in a completely bottom-up fashion, without any prior knowledge beyond a coarse scale prior. Our method learns a tunable image-space prototype of the repeated elements through a reconstruction objective, enabling the model to identify and synthesize consistent object instances within the same image. Experiments on 116 real images from the FSC-147 dataset demonstrate that our method successfully learns coherent element models and captures intra-category variation on challenging images. Qualitative results reveal superior reconstructions and interpretable decompositions compared to classical decomposition, joint alignment, and 3D object modeling methods, while maintaining a simple 2D formulation. These results suggest that meaningful object discovery can emerge from single image learning alone.

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