# DVI: Disentangling Semantic and Visual Identity for Training-Free Personalized Generation

arXiv: https://arxiv.org/abs/2512.18964 · HTML: https://mlsanity.com/paper/2512.18964/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 29 references that are in the map.
- First posted on arXiv 22 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [InstantID: Zero-shot Identity-Preserving Generation in Seconds](https://mlsanity.com/paper/2401.07519/) (2024)
- [PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding](https://mlsanity.com/paper/2312.04461/) (2023)
- [FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention](https://mlsanity.com/paper/2305.10431/) (2023)
- [Less-to-More Generalization: Unlocking More Controllability by In-Context Generation](https://mlsanity.com/paper/2504.02160/) (2025)
- [PuLID: Pure and Lightning ID Customization via Contrastive Alignment](https://mlsanity.com/paper/2404.16022/) (2024)
- [DreamO: A Unified Framework for Image Customization](https://mlsanity.com/paper/2504.16915/) (2025)
- [XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation](https://mlsanity.com/paper/2506.21416/) (2025)
- [UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization](https://mlsanity.com/paper/2408.05939/) (2024)
- [InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity](https://mlsanity.com/paper/2503.16418/) (2025)
- [UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward](https://mlsanity.com/paper/2509.06818/) (2025)
- [FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content](https://mlsanity.com/paper/2308.14256/) (2023)
- [EditID: Training-Free Editable ID Customization for Text-to-Image Generation](https://mlsanity.com/paper/2503.12526/) (2025)
- [EditIDv2: Editable ID Customization with Data-Lubricated ID Feature Integration for Text-to-Image Generation](https://mlsanity.com/paper/2509.05659/) (2025)
- [Smartbanner: Intelligent banner design framework that strikes a balance between creative freedom and design rules](https://mlsanity.com/paper/2303.10325/) (2023)
- [Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model](https://mlsanity.com/paper/2411.06692/) (2024)
- [Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis](https://mlsanity.com/paper/2504.14202/) (2025)

## Built on by (most cited first, 3 of 3)

- [FlexID: Training-Free Flexible Identity Injection via Intent-Aware Modulation for Text-to-Image Generation](https://mlsanity.com/paper/2602.07554/) (2026)
- [Inject Where It Matters: Training-Free Spatially-Adaptive Identity Preservation for Text-to-Image Personalization](https://mlsanity.com/paper/2602.13994/) (2026)
- [Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation](https://mlsanity.com/paper/2605.26111/) (2026)

## Abstract (arXiv)

Recent tuning-free identity customization methods achieve high facial fidelity but often overlook visual context, such as lighting, skin texture, and environmental tone. This limitation leads to ``Semantic-Visual Dissonance,'' where accurate facial geometry clashes with the input's unique atmosphere, causing an unnatural ``sticker-like'' effect. We propose **DVI (Disentangled Visual-Identity)**, a zero-shot framework that orthogonally disentangles identity into fine-grained semantic and coarse-grained visual streams. Unlike methods relying solely on semantic vectors, DVI exploits the inherent statistical properties of the VAE latent space, utilizing mean and variance as lightweight descriptors for global visual atmosphere. We introduce a **Parameter-Free Feature Modulation** mechanism that adaptively modulates semantic embeddings with these visual statistics, effectively injecting the reference's ``visual soul'' without training. Furthermore, a **Dynamic Temporal Granularity Scheduler** aligns with the diffusion process, prioritizing visual atmosphere in early denoising stages while refining semantic details later. Extensive experiments demonstrate that DVI significantly enhances visual consistency and atmospheric fidelity without parameter fine-tuning, maintaining robust identity preservation and outperforming state-of-the-art methods in IBench evaluations.