Evaluating Image Captioning Models with Reference-Free Metrics

In this article, we will explore the latest advancements in "text-to-video synthesis," a technology that can generate videos based on text prompts. This innovative field has made remarkable progress in recent years, thanks to the development of powerful models like the "video latent diffusion model" (VLDM). VLDM can capture motion priors and create high-quality images based on natural language inputs.
One of the challenges with current approaches is that they cannot take an image as input, limiting their ability to generate videos that match the user’s specific vision. To address this issue, we propose a new pipeline called "AnimateDiff," which can finely tune VLDM to handle images as conditioning frames and generate animations according to the user’s intent.
To create engaging videos, it’s essential to understand what users want to convey through their text prompts. We analyze various types of prompts, including those that describe people or objects, landscapes, or scenes. For instance, a prompt like "smiling white hair by atey ghailan" can be interpreted as a motion descriptor (smiling) and a trigger word (white hair).
Another crucial aspect of text-to-video synthesis is demystifying complex concepts. For example, we explain how VLDM captures motion priors by comparing it to a "motion painter," who uses brushstrokes to create dynamic movements on a canvas. This analogy helps users grasp the concept more easily.
In summary, text-to-video synthesis has made tremendous progress in recent years, thanks to advancements like VLDM. Our proposed pipeline, AnimateDiff, can generate animations based on user input and capture their intended meaning through natural language prompts. By demystifying complex concepts and using engaging analogies, we hope to make this technology more accessible and user-friendly for everyone.