Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization

• URL: http://arxiv.org/abs/2512.10955v1
• Date: Thu, 11 Dec 2025 18:59:56 GMT
• Title: Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
• Authors: Tsai-Shien Chen, Aliaksandr Siarohin, Guocheng Gordon Qian, Kuan-Chieh Jackson Wang, Egor Nemchinov, Moayed Haji-Ali, Riza Alp Guler, Willi Menapace, Ivan Skorokhodov, Anil Kag, Jun-Yan Zhu, Sergey Tulyakov,
• Abstract summary: We introduce Omni-Attribute, the first open-vocabulary image attribute encoder to learn attribute-specific representations.<n>We use a dual-objective training paradigm that balances generative fidelity with contrastive disentanglement.<n>The resulting embeddings prove effective for open-vocabulary attribute retrieval, personalization, and compositional generation.
• Score: 82.31106470150844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However, existing methods rely on holistic embeddings from general-purpose image encoders, which entangle multiple visual factors and make it difficult to isolate a single attribute. This often leads to information leakage and incoherent synthesis. To address this limitation, we introduce Omni-Attribute, the first open-vocabulary image attribute encoder designed to learn high-fidelity, attribute-specific representations. Our approach jointly designs the data and model: (i) we curate semantically linked image pairs annotated with positive and negative attributes to explicitly teach the encoder what to preserve or suppress; and (ii) we adopt a dual-objective training paradigm that balances generative fidelity with contrastive disentanglement. The resulting embeddings prove effective for open-vocabulary attribute retrieval, personalization, and compositional generation, achieving state-of-the-art performance across multiple benchmarks.

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