ID-Sim: An Identity-Focused Similarity Metric

• URL: http://arxiv.org/abs/2604.05039v1
• Date: Mon, 06 Apr 2026 18:00:05 GMT
• Title: ID-Sim: An Identity-Focused Similarity Metric
• Abstract summary: ID-Sim is a feed-forward metric designed to faithfully reflect human selective sensitivity.<n>We curate a high-quality training set of images spanning diverse real-world domains, augmented with generative synthetic data.<n>We evaluate our metric on a new unified evaluation benchmark for assessing consistency with human annotations across identity-focused recognition, retrieval, and generative tasks.
• Score: 16.325910041351808
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Humans have remarkable selective sensitivity to identities -- easily distinguishing between highly similar identities, even across significantly different contexts such as diverse viewpoints or lighting. Vision models have struggled to match this capability, and progress toward identity-focused tasks such as personalized image generation is slowed by a lack of identity-focused evaluation metrics. To help facilitate progress, we propose ID-Sim, a feed-forward metric designed to faithfully reflect human selective sensitivity. To build ID-Sim, we curate a high-quality training set of images spanning diverse real-world domains, augmented with generative synthetic data that provides controlled, fine-grained identity and contextual variations. We evaluate our metric on a new unified evaluation benchmark for assessing consistency with human annotations across identity-focused recognition, retrieval, and generative tasks.

Related papers

• PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models [2.260041671242786]
  We study identity unlearning in Arc2Face, a state-of-the-art identity-conditioned latent diffusion model for face generation.<n>We introduce Proximity-guided Identity Unlearning (PIU), an anchor-guided framework for identity unlearning.
  arXiv  Detail & Related papers  (2026-05-21T10:55:56Z)
• AnyID: Ultra-Fidelity Universal Identity-Preserving Video Generation from Any Visual References [52.81991915377852]
  We present AnyID, an ultra-fidelity identity-preservation video generation framework.<n>First, we introduce a scalable omni-referenced architecture that effectively unifies heterogeneous identity inputs into a cohesive representation.<n>Second, we propose a primary-referenced generation paradigm, which designates one reference as a canonical anchor and uses a novel differential prompt to enable precise, attribute-level controllability.
  arXiv  Detail & Related papers  (2026-03-26T08:54:48Z)
• IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation [75.09818147405898]
  IdentityStory is a framework for human-centric story generation that ensures consistent character identity across sequential images.<n>By taming identity-preserving generators, the framework features two key components: Iterative Identity Discovery and Re-denoising Identity Injection.
  arXiv  Detail & Related papers  (2025-12-29T14:54:44Z)
• WithAnyone: Towards Controllable and ID Consistent Image Generation [83.55786496542062]
  Identity-consistent generation has become an important focus in text-to-image research.<n>We develop a large-scale paired dataset tailored for multi-person scenarios.<n>We propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity.
  arXiv  Detail & Related papers  (2025-10-16T17:59:54Z)
• ID-EA: Identity-driven Text Enhancement and Adaptation with Textual Inversion for Personalized Text-to-Image Generation [33.84646269805187]
  ID-EA is a novel framework that guides text embeddings to align with visual identity embeddings.<n> ID-EA substantially outperforms state-of-the-art methods in identity preservation metrics.<n>It generates personalized portraits 15 times faster than existing approaches.
  arXiv  Detail & Related papers  (2025-07-16T07:42:02Z)
• From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization [9.614305363044737]
  Person re-identification (ReID) aims to extract accurate identity representation features.<n>We propose a Training-Free Feature Centralization ReID framework (Pose2ID) to reduce individual noise.<n>Our method sets new state-of-the-art results across standard, cross-modality, and occluded ReID tasks.
  arXiv  Detail & Related papers  (2025-03-02T15:31:48Z)
• Disentangled Representations for Short-Term and Long-Term Person Re-Identification [33.76874948187976]
  We propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN) It disentangles identity-related and unrelated features from person images through an identity-shuffling technique. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks.
  arXiv  Detail & Related papers  (2024-09-09T02:09:49Z)
• ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning [57.91881829308395]
  Identity-preserving text-to-image generation (ID-T2I) has received significant attention due to its wide range of application scenarios like AI portrait and advertising. We present textbfID-Aligner, a general feedback learning framework to enhance ID-T2I performance.
  arXiv  Detail & Related papers  (2024-04-23T18:41:56Z)
• X-ReID: Cross-Instance Transformer for Identity-Level Person Re-Identification [53.047542904329866]
  Cross Intra-Identity Instances module (IntraX) fuses different intra-identity instances to transfer Identity-Level knowledge. Cross Inter-Identity Instances module (InterX) involves hard positive and hard negative instances to improve the attention response to the same identity.
  arXiv  Detail & Related papers  (2023-02-04T03:16:18Z)
• Protecting Celebrities with Identity Consistency Transformer [119.67996461810304]
  Identity Consistency Transformer focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions. We show that Identity Consistency Transformer exhibits superior generalization ability not only across different datasets but also across various types of image degradation forms found in real-world applications including deepfake videos.
  arXiv  Detail & Related papers  (2022-03-02T18:59:58Z)
• Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification [39.605062525247135]
  We propose a semantic consistency and identity mapping multi-component generative adversarial network (SC-IMGAN) which provides style adaptation from one to many domains. Our proposed method outperforms state-of-the-art techniques on six challenging person Re-ID datasets.
  arXiv  Detail & Related papers  (2021-04-28T14:12:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.