Researchers from ByteDance Inc. and UC Berkeley have developed Video Custom Diffusion (VCD), a framework for generating subject identity-controllable videos. VCD employs an ID module for precise identity extraction, 3D Gaussian Noise Prior for inter-frame consistency, and V2V modules to enhance video quality. The framework has shown superiority over existing methods in preserving high-quality video identity.
Revolutionizing Video Generation with Customized Identity
Text-to-image (T2I) and text-to-video (T2V) generation have seen significant advancements in generative models. While T2I models excel at controlling subject identity, extending this capability to T2V has been challenging. Existing T2V methods struggle with precise control over generated content, especially for human-related scenarios. ByteDance Inc. and UC Berkeley have developed Video Custom Diffusion (VCD), a powerful framework addressing these challenges.
The VCD Framework
VCD employs three key components: an ID module for precise identity extraction, a 3D Gaussian Noise Prior for inter-frame consistency, and V2V modules to enhance video quality. By disentangling identity information from background noise, VCD aligns IDs accurately, ensuring stable video outputs. The framework’s flexibility allows seamless integration with various AI-generated content models.
Practical Advancements
The VCD model maintains character identity across various realistic and stylized models. It ensures high-quality, identity-preserving video generation. The researchers meticulously selected subjects from diverse datasets and evaluated the method against multiple baselines using CLIP-I and DINO for identity alignment, text alignment, and temporal smoothness. The training details involved using Stable Diffusion 1.5 for the ID module and adjusting learning rates and batch sizes accordingly.
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