Omni-Attribute: Open-vocabulary Attribute Encoder for Visual Concept Personalization
Authors: name: Tsai-Shien Chen; name: Aliaksandr Siarohin; name: Guocheng Gordon Qian; name: Kuan-Chieh Jackson Wang; name: Egor Nemchinov; name: Moayed Haji-Ali; name: Riza Alp Guler; name: Willi Menapace; name: Ivan Skorokhodov; name: Anil Kag; name: Jun-Yan Zhu; name: Sergey Tulyakov
Published: 2025-12-11 18:59:56

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.