Project Title
vid2vid — High-Resolution Photorealistic Video-to-Video Translation with PyTorch
Overview
vid2vid is a PyTorch implementation for high-resolution, photorealistic video-to-video translation. It enables the transformation of semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses. This project stands out for its ability to handle high-resolution videos and its applications in various domains, such as urban planning, entertainment, and virtual reality.
Key Features
- High-resolution video-to-video translation (e.g., 2048x1024)
- Supports various translation tasks, including label-to-streetview, edge-to-face, and pose-to-body
- Built on top of image-to-image translation techniques, leveraging the capabilities of pix2pixHD and SPADE
Use Cases
- Urban planners can use vid2vid to visualize how urban areas might look in the future based on semantic label maps.
- Filmmakers and game developers can use it to create realistic character animations from simple edge maps or poses.
- Researchers in computer vision can utilize vid2vid for generating synthetic training data for various tasks.
Advantages
- High-resolution output, suitable for professional applications
- Flexible, supporting a range of video-to-video translation tasks
- Leveraging advanced image-to-image translation techniques for improved results
Limitations / Considerations
- Requires a powerful NVIDIA GPU with CUDA and cuDNN for optimal performance
- The project is research-oriented and may require significant computational resources for training and testing
- The complexity of the model might lead to longer training times and higher memory usage
Similar / Related Projects
- pix2pixHD: A high-resolution image-to-image translation project by NVIDIA, which vid2vid builds upon.
- SPADE: A method for semantic image synthesis and inpainting, also used in vid2vid.
- DeepVideoPortraits: A project focused on creating high-quality video portraits, which shares similarities in the application of video manipulation.
Basic Information
- GitHub: https://github.com/NVIDIA/vid2vid
- Stars: 8,700
- License: Unknown
- Last Commit: 2025-10-04
📊 Project Information
- Project Name: vid2vid
- GitHub URL: https://github.com/NVIDIA/vid2vid
- Programming Language: Python
- ⭐ Stars: 8,700
- 🍴 Forks: 1,210
- 📅 Created: 2018-08-14
- 🔄 Last Updated: 2025-10-04
🏷️ Project Topics
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