Stand-In: Identity-Preserving Video Generation
A lightweight, plug-and-play framework for identity-preserving video generation that maintains character consistency while creating high-quality videos from text prompts.
What is Stand-In?
Stand-In is a specialized framework designed for identity-preserving video generation that addresses one of the most challenging problems in AI video creation: maintaining consistent character identity across video frames. Traditional text-to-video models often struggle with character consistency, leading to videos where the same person appears different in various frames or scenes.
This framework represents a significant advancement in video generation technology by introducing a lightweight approach that requires training only 1% additional parameters compared to base video generation models. Despite this minimal parameter increase, Stand-In achieves state-of-the-art results in both face similarity and naturalness metrics, outperforming various full-parameter training methods.
The core innovation of Stand-In lies in its ability to integrate seamlessly with existing text-to-video models without requiring complete model retraining. This plug-and-play nature makes it highly accessible for researchers and developers who want to enhance their video generation capabilities with identity preservation features.
Stand-In supports multiple applications beyond basic identity preservation, including subject-driven video generation, pose-controlled video generation, video stylization, and face swapping. This versatility makes it a comprehensive solution for various video generation tasks that require character consistency.
Overview of Stand-In
| Feature | Description |
|---|---|
| Framework Type | Identity-Preserving Video Generation |
| Architecture | Lightweight, Plug-and-Play |
| Training Efficiency | Only 1% additional parameters |
| Compatibility | Works with existing T2V models |
| Model Size | 153M parameters (v1.0) |
| Base Model | Wan2.1-14B-T2V compatible |
| Research Paper | arXiv:2508.07901 |
Key Features of Stand-In
Efficient Training
Stand-In requires training only 1% of the base model parameters, making it extremely resource-efficient compared to traditional approaches that require full model retraining. This efficiency enables faster deployment and reduces computational costs significantly.
High Fidelity Identity Preservation
Achieves outstanding identity consistency without sacrificing video generation quality. The framework maintains facial features, expressions, and other identity markers across all frames while preserving the natural movement and dynamics of the generated video.
Plug-and-Play Integration
Easily integrates into existing text-to-video models without requiring architectural changes. This compatibility allows developers to enhance their current video generation systems with identity preservation capabilities quickly and efficiently.
Community Model Compatibility
Compatible with community models such as LoRA and supports various downstream video tasks. This extensibility makes Stand-In valuable for a wide range of applications and allows integration with existing model ecosystems.
Multi-Task Support
Supports subject-driven video generation, pose-controlled video generation, video stylization, and face swapping. This versatility makes it a comprehensive solution for various video generation tasks requiring character consistency.
Non-Human Subject Support
Works effectively with non-human subjects including animated characters, cartoon figures, and stylized representations, making it suitable for creative applications beyond realistic human video generation.
How Stand-In Works
Step 1: Reference Image Input
The process begins with providing a high-resolution frontal face image as the reference identity. This image serves as the identity anchor that will be preserved throughout the generated video. The system accepts various image formats and resolutions, with built-in preprocessing to optimize the input.
Step 2: Text Prompt Processing
Users provide a text description of the desired video scene. The prompt can be in English or Chinese and should focus on scene description rather than appearance details. For optimal results, use generic terms like "a man" or "a woman" without additional facial feature descriptions.
Step 3: Identity Integration
Stand-In processes the reference image through its identity preservation module, extracting key facial features and identity markers. These features are then integrated into the video generation pipeline using the lightweight adapter network.
Step 4: Video Generation
The enhanced text-to-video model generates frames while maintaining consistency with the reference identity. The plug-and-play architecture ensures that identity features are preserved across all frames while maintaining natural movement and scene dynamics.
Applications and Use Cases
Content Creation
Generate personalized video content for social media, marketing campaigns, and digital storytelling while maintaining character consistency across scenes.
Film and Animation
Create preliminary video sequences for film pre-production, character testing, and storyboard visualization with consistent character appearance.
Educational Content
Develop educational videos with consistent virtual instructors or historical figures for immersive learning experiences.
Virtual Avatars
Create dynamic virtual representations for gaming, virtual reality experiences, and digital communication platforms.
Face Swapping
Perform video face swapping with experimental capabilities, allowing identity transfer while maintaining natural expressions and movements.
Stylized Content
Generate videos in various artistic styles while preserving character identity, compatible with community LoRA models for style transfer.
Technical Specifications
System Requirements
- Python: 3.11+
- GPU: CUDA compatible
- Memory: Sufficient for model loading
- Storage: Space for model checkpoints
Model Components
- Base Model: Wan2.1-T2V-14B
- Face Recognition: AntelopeV2
- Stand-In Module: 153M parameters
- Format Support: Multiple video formats
Advantages Over Traditional Methods
Stand-In Approach
- ✓Only 1% additional parameters needed
- ✓Plug-and-play integration
- ✓Maintains video quality
- ✓Fast training and inference
- ✓Community model compatible
Traditional Methods
- ✗Require full model retraining
- ✗Complex integration process
- ✗May compromise video quality
- ✗Resource-intensive training
- ✗Limited compatibility
Performance and Results
Stand-In demonstrates superior performance across multiple evaluation metrics, achieving state-of-the-art results in identity preservation while maintaining high video quality. The framework has been tested extensively on various scenarios including different lighting conditions, poses, and expressions.
Face Similarity
Superior
Outperforms full-parameter training methods
Video Naturalness
High Quality
Maintains natural movement and expressions
Training Efficiency
99% Reduction
In trainable parameters compared to full training
Getting Started with Stand-In
Quick Start Guide
- Clone the repository and set up the environment
- Download the required model weights using the provided script
- Prepare your reference image and text prompt
- Run the inference script with your inputs
- Review and save your generated video
Best Practices
- Use high-resolution frontal face images for best results
- Keep prompts focused on scene description rather than appearance
- Experiment with different denoising strengths for face swapping
- Combine with community LoRA models for stylized outputs
Frequently Asked Questions
Future Developments
Planned Features
- • Enhanced ComfyUI integration
- • Improved face swapping capabilities
- • Support for Wan2.2-T2V-A14B model
- • Training dataset and code release
- • Extended community model support
Research Directions
- • Multi-character consistency
- • Improved temporal stability
- • Cross-domain identity transfer
- • Real-time processing optimization
- • Enhanced style preservation