Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
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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Tsai-Shien Chen
claimed_verified
2025-12-15T08:13:17.349Z
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Tsai-Shien Chen
tsaishien-chen
user
tsaishien-chen
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Aliaksandr Siarohin
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Guocheng Gordon Qian
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Kuan-Chieh Jackson Wang
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Egor Nemchinov
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Moayed Haji-Ali
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Riza Alp Guler
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Willi Menapace
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Ivan Skorokhodov
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Anil Kag
claimed_verified
2026-03-17T08:32:10.223Z
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Anil Kag
akag-snap
user
akag-snap
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Jun-Yan Zhu
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Sergey Tulyakov
Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
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.
Omni-Attribute, an open-vocabulary image attribute encoder, learns attribute-specific representations to enable precise visual concept personalization and compositional generation.
image attribute encoder high-fidelity attribute-specific representations semantically linked image pairs dual-objective training paradigm generative fidelity contrastive disentanglement open-vocabulary attribute retrieval visual concept personalization compositional generation