

Pippo
Overview :
Pippo, developed in collaboration between Meta Reality Labs and various universities, is a generative model capable of producing high-resolution, multi-view videos from a single ordinary photograph. Its core advantage lies in generating high-quality 1K resolution videos without any additional input (such as parameterized models or camera parameters). Based on a multi-view diffusion transformer architecture, it has broad application prospects in areas like virtual reality and film production. Pippo's code is open-source, but pre-trained weights are not included; users need to train the model themselves.
Target Users :
Pippo is ideal for researchers and developers, particularly those specializing in computer vision, image generation, and virtual reality. It provides them with a powerful tool to explore techniques for generating high-quality videos from single images, applicable to scenarios such as film production and virtual reality content development.
Use Cases
Researchers use the Pippo model to generate high-quality, multi-view videos from single photographs for virtual reality content creation.
Film production teams leverage Pippo to generate high-resolution virtual character videos, saving on filming costs.
Developers extend the code architecture of Pippo to develop new image generation applications.
Features
Generates high-resolution, multi-view videos from a single photograph.
Supports model training at different resolutions (128, 512, 1024).
Provides sample training code and dataset support (e.g., Ava-256).
Calculates reprojection error between generated and real images.
Offers techniques for controlling MLP and attention bias to optimize diffusion transformer performance.
Supports running on different GPU configurations (e.g., A100, T4).
How to Use
1. Clone the repository: `git clone git@github.com:facebookresearch/pippo.git` and navigate to the directory.
2. Set up the environment: Create a Conda environment and install dependencies, such as PyTorch and other libraries.
3. Download sample data: Run `python scripts/pippo/download_samples.py` to download a sample of the Ava-256 dataset.
4. Start training: Choose an appropriate model configuration file based on your GPU configuration and run `python train.py` to begin training.
5. Calculate reprojection error: Run `python scripts/pippo/reprojection_error.py` to compare the error between generated and real images.
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