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GD-VDM

This repository contains the code for training and running the Generated Depth - Video Diffusion Model (GD-VDM). The GD-VDM consists of several steps, which are explained below.

Prerequisites

Before running the script, ensure that you modify the necessary parameters and paths to fit your requirements.

Steps

The main script serves as the entry point for training the GD-VDM. It encompasses the entire training procedure, which includes the following steps:

1. Training the Video Diffusion Model (VDM) on depth videos.
2. Creating a "denoised" depth video dataset:

• Apply forward diffusion noise to the original depth videos.
• Denoise the videos using the pre-trained denoising model of the VDM.

3. Training the Video to Video Diffusion Model (Vid2Vid-DM) on a paired photorealistic- "denoised" depth video dataset:

• The Vid2Vid-DM learns the mapping from "denoised" depth videos to photorealistic real-world videos.
• Load the weights of the VDM, trained on depth map videos, into the Depth U-Net of the Vid2Vid model, but do not freeze them.

4. Generating samples using the GD-VDM framework:

• Synthesize depth videos using the VDM trained on depth videos.
• Pass the generated depth videos to the Vid2Vid-DM to create photorealistic real-world videos.

Note: You can also run the individual scripts for each step in the procedure if you want to perform specific tasks separately.

Enjoy using GD-VDM!