# Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation

arXiv: https://arxiv.org/abs/2508.07901 · HTML: https://mlsanity.com/paper/2508.07901/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 55 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 30 of 49)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers](https://mlsanity.com/paper/2401.08740/) (2024)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [Open-Sora: Democratizing Efficient Video Production for All](https://mlsanity.com/paper/2412.20404/) (2024)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation](https://mlsanity.com/paper/2310.05737/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [InstantID: Zero-shot Identity-Preserving Generation in Seconds](https://mlsanity.com/paper/2401.07519/) (2024)
- [LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models](https://mlsanity.com/paper/2309.15103/) (2023)
- [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)

## Built on by (most cited first, 28 of 28)

- [Helios: Real Real-Time Long Video Generation Model](https://mlsanity.com/paper/2603.04379/) (2026)
- [RefAlign: Representation Alignment for Reference-to-Video Generation](https://mlsanity.com/paper/2603.25743/) (2026)
- [Lynx: Towards High-Fidelity Personalized Video Generation](https://mlsanity.com/paper/2509.15496/) (2025)
- [Scaling Zero-Shot Reference-to-Video Generation](https://mlsanity.com/paper/2512.06905/) (2025)
- [DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer](https://mlsanity.com/paper/2601.01425/) (2026)
- [Identity-Preserving Image-to-Video Generation via Reward-Guided Optimization](https://mlsanity.com/paper/2510.14255/) (2025)
- [EgoX: Egocentric Video Generation from a Single Exocentric Video](https://mlsanity.com/paper/2512.08269/) (2025)
- [ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation](https://mlsanity.com/paper/2606.11670/) (2026)
- [OmniTransfer: All-in-one Framework for Spatio-temporal Video Transfer](https://mlsanity.com/paper/2601.14250/) (2026)
- [FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization](https://mlsanity.com/paper/2605.15824/) (2026)
- [Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding](https://mlsanity.com/paper/2601.01352/) (2026)
- [WildActor: Unconstrained Identity-Preserving Video Generation](https://mlsanity.com/paper/2603.00586/) (2026)
- [Rethinking Position Embedding as a Context Controller for Multi-Reference and Multi-Shot Video Generation](https://mlsanity.com/paper/2604.03738/) (2026)
- [LibraGen: Playing a Balance Game in Subject-Driven Video Generation](https://mlsanity.com/paper/2603.13506/) (2026)
- [Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment](https://mlsanity.com/paper/2607.04311/) (2026)
- [GenHOI: Towards Object-Consistent Hand-Object Interaction with Temporally Balanced and Spatially Selective Object Injection](https://mlsanity.com/paper/2603.06048/) (2026)
- [Identity as Presence: Towards Appearance and Voice Personalized Joint Audio-Video Generation](https://mlsanity.com/paper/2603.17889/) (2026)
- [Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation](https://mlsanity.com/paper/2604.25819/) (2026)
- [MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation](https://mlsanity.com/paper/2605.13857/) (2026)
- [HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enhancement](https://mlsanity.com/paper/2607.18217/) (2026)
- [ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis](https://mlsanity.com/paper/2603.19925/) (2026)
- [FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation](https://mlsanity.com/paper/2605.04702/) (2026)
- [Vera: Identity-Faithful Human Subject-to-Video Generation](https://mlsanity.com/paper/2607.20247/) (2026)
- [KeyID: Decoupled Drafting and Keyframe Editing for Identity-Preserving Video Generation](https://mlsanity.com/paper/2608.16154/) (2026)
- [BEACON: Behavior and Appearance Control for Subject-Specific Video Generation](https://mlsanity.com/paper/2609.13264/) (2026)
- [OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to-Video Generation](https://mlsanity.com/paper/2609.22069/) (2026)
- [LongLive-Plug: Once-for-All Distillation for Video Generation](https://mlsanity.com/paper/2609.38154/) (2026)
- [Custom Forcing: Training-Free Subject Customization for Autoregressive Video Generation](https://mlsanity.com/paper/2610.02914/) (2026)

## Abstract (arXiv)

Generating high-fidelity human videos that match user-specified identities is important yet challenging in the field of generative AI. Existing methods often rely on an excessive number of training parameters and lack compatibility with other AIGC tools. In this paper, we propose Stand-In, a lightweight and plug-and-play framework for identity preservation in video generation. Specifically, we introduce a conditional image branch into the pre-trained video generation model. Identity control is achieved through restricted self-attentions with conditional position mapping. Thanks to these designs, which greatly preserve the pre-trained prior of the video generation model, our approach is able to outperform other full-parameter training methods in video quality and identity preservation, even with just ~1% additional parameters and only 2000 training pairs. Moreover, our framework can be seamlessly integrated for other tasks, such as subject-driven video generation, pose-referenced video generation, stylization, and face swapping.