Generative AI models are machine learning models that can generate new data samples that are similar to the data they were trained on. They can be used to create new images, music, or even text. Generative AI models are a subset of deep learning models that use a type of neural network called a generative adversarial network (GAN) or a variational autoencoder (VAE) to generate new data.
Creating a generative video model requires a significant amount of expertise and computational resources. Here are some general steps you could follow:
Collect a dataset of videos to use as training data. The dataset should be diverse and include examples of the type of videos you want to generate.
Preprocess the data by resizing the videos to a consistent size, normalizing the pixel values, and extracting the individual frames.
Train a generative model on the preprocessed data. You could use a GAN or VAE to create a model that learns to generate new video frames that are similar to the input data.
Evaluate the model to see how well it performs at generating new videos. You could use metrics such as structural similarity index (SSIM) or peak signal-to-noise ratio (PSNR) to measure how similar the generated videos are to the input data.
Once you are satisfied with the performance of the model, use it to generate new videos by feeding it random noise as input and letting it generate new video frames. You can then combine these frames into a full video.
It’s worth noting that creating a generative video model is a complex task that requires expertise in machine learning, computer vision, and video processing. If you are new to these fields, you may want to start with simpler generative models such as those that generate images or text before attempting to create a video model.
Edith Heroux Video Blog 