BeyondFacial: Identity-Preserving Personalized Generation Beyond Facial Close-ups

TL;DR

This paper introduces a novel IPPG method that overcomes facial close-up limitations by separating identity and semantic features, enabling more natural, scene-rich personalized generation without manual masking or fine-tuning.

Contribution

It proposes a dual-line inference pipeline with identity-semantic separation, an adaptive fusion strategy, and an identity aggregation module, advancing beyond facial-centric approaches in personalized image generation.

Findings

Achieves stable, effective identity preservation in scene-rich images.

Enables efficient, plug-and-play integration into existing frameworks.

Facilitates film-level character-scene creation without manual masking.

Abstract

Identity-Preserving Personalized Generation (IPPG) has advanced film production and artistic creation, yet existing approaches overemphasize facial regions, resulting in outputs dominated by facial close-ups.These methods suffer from weak visual narrativity and poor semantic consistency under complex text prompts, with the core limitation rooted in identity (ID) feature embeddings undermining the semantic expressiveness of generative models. To address these issues, this paper presents an IPPG method that breaks the constraint of facial close-ups, achieving synergistic optimization of identity fidelity and scene semantic creation. Specifically, we design a Dual-Line Inference (DLI) pipeline with identity-semantic separation, resolving the representation conflict between ID and semantics inherent in traditional single-path architectures. Further, we propose an Identity Adaptive Fusion (IdAF) strategy that defers ID-semantic fusion to the noise prediction stage, integrating adaptive attention fusion and noise decision masking to avoid ID embedding interference on semantics without manual masking. Finally, an Identity Aggregation Prepending (IdAP) module is introduced to aggregate ID information and replace random initializations, further enhancing identity preservation. Experimental results validate that our method achieves stable and effective performance in IPPG tasks beyond facial close-ups, enabling efficient generation without manual masking or fine-tuning. As a plug-and-play component, it can be rapidly deployed in existing IPPG frameworks, addressing the over-reliance on facial close-ups, facilitating film-level character-scene creation, and providing richer personalized generation capabilities for related domains.