TriAnchor-ID: Multi-View Semantic Identity Anchoring for Pose-Aware Personalized Diffusion

This paper presents TriAnchor-ID, a prototype framework for identity-conditioned diffusion portrait generation. The work focuses on multi-view semantic identity anchoring using ArcFace/InsightFace embeddings, quality-weighted identity aggregation, landmark-derived geometric observations, and post-generation identity consistency evaluation. The paper corrects an important distinction between semantic face identity embeddings and physical 3D morphable model parameters: the implemented 512-dimensional ArcFace vector is treated as a semantic identity anchor, not as a FLAME or 3DMM shape vector. The proposed Identity Capsule design separates implemented components from future extensions such as FLAME/DECA shape fitting, UV texture modeling, spatial ControlNet conditioning, and inference-time identity consistency guidance. This version is released as a preprint/prototype research report. The qualitative examples are illustrative, and the paper outlines a controlled evaluation protocol using ArcFace similarity, identity drift score, landmark error, face detection failure rate, prompt alignment, repeated seeds, and confidence intervals.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-28
DOI
https://doi.org/10.5281/zenodo.19836221
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
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article

TriAnchor-ID: Multi-View Semantic Identity Anchoring for Pose-Aware Personalized Diffusion

Akash Kumar
Zenodo (CERN European Organization for Nuclear Research)
Face recognition and analysis
article

TriAnchor-ID: Multi-View Semantic Identity Anchoring for Pose-Aware Personalized Diffusion

Akash Kumar
article en

Abstract

This paper presents TriAnchor-ID, a prototype framework for identity-conditioned diffusion portrait generation. The work focuses on multi-view semantic identity anchoring using ArcFace/InsightFace embeddings, quality-weighted identity aggregation, landmark-derived geometric observations, and post-generation identity consistency evaluation. The paper corrects an important distinction between semantic face identity embeddings and physical 3D morphable model parameters: the implemented 512-dimensional ArcFace vector is treated as a semantic identity anchor, not as a FLAME or 3DMM shape vector. The proposed Identity Capsule design separates implemented components from future extensions such as FLAME/DECA shape fitting, UV texture modeling, spatial ControlNet conditioning, and inference-time identity consistency guidance. This version is released as a preprint/prototype research report. The qualitative examples are illustrative, and the paper outlines a controlled evaluation protocol using ArcFace similarity, identity drift score, landmark error, face detection failure rate, prompt alignment, repeated seeds, and confidence intervals.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 4%
Face recognition and analysis
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