Welcome to the world of video inpainting, where you can seamlessly remove unwanted objects from videos without extensive training or massive datasets! This guide breaks down how to utilize the implicit internal video inpainting technique introduced in our ICCV2021 paper, designed for easy implementation. Get ready to transform your video editing experience!

What You Need to Get Started

• A compatible computer with suitable hardware for tensorflow.
• The necessary software installed on your environment (as detailed below).
• Some patience while training your models!

Installation Guide

First, you need to set up your working environment. Here’s a step-by-step guide:

git clone https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting.git
cd Implicit-Internal-Video-Inpainting

Environment Setup

This project is built on TensorFlow 2.x, and can be set up using Anaconda:

conda create -n IIVI python=3.7
conda activate IIVI
conda install tensorflow-gpu tensorboard
pip install pyaml opencv-python
pip install tensorflow-addons

Alternatively, utilize the provided environment file:

conda env create -f environment.yml
conda activate IIVI

Usage Instructions

Quick Start with Example Sequence

After setup, you can quickly start with the sample sequence:

python train.py

This command initiates the training process; by default, it runs for 50,000 iterations, taking about 4 hours with a single GPU. During this process, you can monitor training using Tensorboard:

tensorboard --logdir .explogs

Your Own Data Processing

If you want to work with your data, here’s how to prepare it:

• Dilate object masks first to avoid artifacts:
  

  python scripts/preprocess_mask.py --annotation_path inputs/annotations/bmx-trees

• Modify configtrain.yml for video path, log path, and other parameters, and then train:

python train.py

Improving Results

If initial results are not satisfactory, consider fine-tuning with additional loss parameters:

python train.py

Multi-GPU Training

For faster training, you can choose to use multiple GPUs:

python train_dist.py

Ensure you configure configtrain.yml appropriately.

Understanding the Propagation Technique Through Analogy

Imagine you have a painting of a landscape but accidentally splattered paint on one of the trees. Instead of repainting the entire scene, you could take a small image of the tree from a nearby area and replace the affected one, ensuring it blends in seamlessly.

This method represents what our internal video inpainting technique does—using the information from a single frame (your pristine tree) to fill in gaps in others (the spoiled tree), without starting from scratch each time!

Troubleshooting

Should you encounter issues during installation or while running the code, consider the following tips:

• Ensure all dependencies are installed correctly.
• Check whether TensorFlow is compatible with your hardware.
• Inspect log files for error messages that can guide you to the solution.
• For further assistance in resolving issues, consult our project page or reach out if needed.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

That’s it! You now have a comprehensive understanding of the implicit internal video inpainting method. You can explore further, utilize your datasets, and even modify control parameters based on your needs for optimal results.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.