Training Free Stylized Abstraction

• URL: http://arxiv.org/abs/2505.22663v2
• Date: Tue, 01 Jul 2025 17:38:27 GMT
• Title: Training Free Stylized Abstraction
• Authors: Aimon Rahman, Kartik Narayan, Vishal M. Patel,
• Abstract summary: Stylized abstraction synthesizes visually exaggerated yet semantically faithful representations of subjects, balancing recognizability with perceptual distortion.<n>We propose a training-free framework that generates stylized abstractions from a single image using inference-time scaling in vision-language models (VLLMs)<n>Our method adapts structural restoration dynamically through style-aware temporal scheduling, enabling high-fidelity reconstructions that honor both subject and style.
• Score: 27.307331773270676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stylized abstraction synthesizes visually exaggerated yet semantically faithful representations of subjects, balancing recognizability with perceptual distortion. Unlike image-to-image translation, which prioritizes structural fidelity, stylized abstraction demands selective retention of identity cues while embracing stylistic divergence, especially challenging for out-of-distribution individuals. We propose a training-free framework that generates stylized abstractions from a single image using inference-time scaling in vision-language models (VLLMs) to extract identity-relevant features, and a novel cross-domain rectified flow inversion strategy that reconstructs structure based on style-dependent priors. Our method adapts structural restoration dynamically through style-aware temporal scheduling, enabling high-fidelity reconstructions that honor both subject and style. It supports multi-round abstraction-aware generation without fine-tuning. To evaluate this task, we introduce StyleBench, a GPT-based human-aligned metric suited for abstract styles where pixel-level similarity fails. Experiments across diverse abstraction (e.g., LEGO, knitted dolls, South Park) show strong generalization to unseen identities and styles in a fully open-source setup.

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