Stand-In - A video generation framework launched by Tencent WeChat

What is Stand-In?

Stand-In is a lightweight video generation framework developed by Tencent WeChat's vision team, focusing on generating identity-protected videos. By training only 1% of the base model parameters, the framework can generate high-fidelity, identity-consistent videos. It is plug-and-play and can be easily integrated into existing text-to-video (T2V) models. Stand-In supports various application scenarios, including identity-preserving text-to-video generation, non-human subject video generation, stylized video generation, face-swapping, and pose-guided video generation, offering advantages such as high efficiency, flexibility, and strong scalability.

Main functions of Stand-In

• Identity-preserving text-to-video generationBased on text descriptions and reference images, generate videos that match the identities in the reference images, ensuring that the characteristics of the people in the videos remain highly consistent.
• Non-human subject-preserving video generationThe framework can generate videos of non-human subjects such as cartoon characters and objects while maintaining the consistency of the subject's features.
• Identity-preserving stylized video generationWhile maintaining the characteristics of the characters, a specific artistic style, such as oil painting or animation style, is applied to the generated video to achieve a stylized effect.
• Video face swapThis feature replaces the faces of people in a video with those in a reference image, achieving high-fidelity face replacement while maintaining the naturalness and coherence of the video.
• Posture-guided video generationBased on the input pose sequence, it generates a video of a person in the corresponding pose, achieving precise control and generation of poses.

The technical principle of Stand-In

• Conditional image branchingA conditional image branch is introduced into the pre-trained video generation model. The reference image is encoded into the same latent space as the video using a pre-trained VAE (Variational Autoencoder), extracting rich facial features.
• Restricted self-attention mechanismIdentity control is achieved through a restricted self-attention mechanism, enabling video features to effectively reference identity information in reference images while maintaining the independence of the reference images. Conditional position mapping is used to distinguish between image and video features, ensuring the accuracy and efficiency of information exchange.
• Low-rank adaptation (LoRA)Low-rank adaptation (LoRA) is used in the conditional image branch to enhance the model's ability to utilize identity information while maintaining the model's lightweight design. LoRA only fine-tunes the QKV projection of the conditional image, avoiding the introduction of too many training parameters.
• KV CacheThe reference image's time step is fixed at zero, and the Key and Value matrices remain unchanged during diffusion denoising. These matrices are cached during inference to accelerate computation.
• Lightweight designStand-In training requires approximately 1% more parameters, significantly reducing training costs and computational resource consumption. It also allows Stand-In to be easily integrated into existing text-to-video (T2V) models, offering high scalability and compatibility.

Stand-In project address

• Project official websitehttps://www.stand-in.tech/
• GitHub repository: https://github.com/WeChatCV/Stand-In
• HuggingFace model libraryhttps://huggingface.co/BowenXue/Stand-In
• arXiv technical paper: https://arxiv.org/pdf/2508.07901

Stand-In application scenarios

• Virtual character generationCreate virtual characters for movies, TV series, and animations to ensure consistency of character identity across different scenarios.
• Special effects compositingIn special effects production, it can quickly generate virtual characters that match the identities of live actors, reducing the complexity of post-production compositing.
• Personalized advertisingIt generates personalized ad videos based on reference images provided by users, thereby increasing the attractiveness of ads and user engagement.
• Virtual spokespersonCreate virtual spokespeople for brand promotion and product advertising to ensure the consistency and coherence of the brand image.
• Character customizationPlayers can generate in-game characters based on their own appearance, enhancing the game's immersion and personalized experience.