ViViD
V
Vivid
Overview :
ViViD is a new framework for video virtual try-on based on diffusion models. It designs a clothing encoder to extract fine-grained clothing semantic features, and introduces a lightweight pose encoder to ensure temporal consistency, generating realistic video try-on effects. ViViD has collected the largest-scale, most diverse clothing types, and highest resolution video virtual try-on dataset to date.
Target Users :
ViViD is suitable for fashion retailers, clothing designers, and video content creators. They can use this technology to provide customers with virtual try-on experiences, enhancing the interactivity and realism of online shopping.
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Use Cases
Online retailers use ViViD to provide personalized virtual try-on services, attracting customers and boosting sales.
Clothing designers leverage ViViD to showcase new designs, attracting potential buyers.
Video content creators utilize ViViD to increase the interactivity and entertainment value of their videos.
Features
Clothing Encoder: Extracts fine-grained semantic features of clothing.
Attentional Feature Fusion Mechanism: Injects clothing details into the target video.
Pose Encoder: Encodes pose signals and learns the interaction between clothing and human pose.
Temporal Module: Inserts text into the image-to-image stable diffusion model to generate coherent and realistic videos.
Massive Dataset: Provides diversified clothing types and high-resolution video try-on data.
Open Access: Code, datasets, and weights will be publicly available.
How to Use
1. Visit the ViViD project page and download the required code and datasets.
2. Install the necessary dependencies and environment according to the provided documentation.
3. Run the clothing encoder to extract clothing features.
4. Process the target video using the pose encoder to extract human pose information.
5. Use the ViViD model to fuse clothing features into the target video.
6. Adjust parameters to optimize the video try-on effect.
7. Output the final virtual try-on video.
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