The development of text-to-image generation models is one of the best advancements in Artificial Intelligence. DALLE 2, the recently developed model introduced by OpenAI, creates wonderful images from textual descriptions or prompts. This diffusion model learns to produce data by reversing a gradual noising process. Using diffusion modeling, the model works by ruining the images and then trying to reconstruct them. Currently, various such models have the ability to generate a fresh image from a textual explanation and also edit an existing image.
With the increasing popularity of image diffusion models for generating high-quality, diverse images, a lot of new methods and developments are getting introduced. These models invert real images as well as produce images based on textual prompts, making them suitable for different image editing applications. In a recent paper, researchers have proposed an approach called Pix2Video that can perform video editing using image diffusion. Research has been conducted on how to use pre-trained image models for video editing based on text prompts. Their goal is to edit a video while also preserving the content and important details of the video.
The team has proposed a two-step methodology. First, a pre-trained structure-guided image diffusion model is used to perform text-guided edits on an anchor frame. Second, the team has introduced a key step where they progressively propagate the changes to the future frames using a technique called self-attention feature injection. Self-attention is basically a mechanism that allows a model to weigh the signification of different parts of an input sequence when processing it. This mechanism is then used to regulate which parts of the anchor frame should be propagated to the future frames and how to adapt the core denoising step of the diffusion model to achieve this.
Pix2Video is training-free as it does not require any additional training data or pre-processing. It is versatile and can be applied to a wide range of video edits. Pix2Video has been evaluated on various real video clips demonstrating local and global edits. It has even been compared to several state-of-the-art approaches and successfully performed equally or better. It performed seemingly well without needing any compute-intensive pre-processing method or finetuning approach specific to the video.
The researchers evaluated Pix2Video on a dataset called DAVIS which consists of videos with 50 to 82 frames. Pix2Video was compared to three other methods – The first method, proposed by Jamriska et al., propagates the style of a set of given frames to the input video clip. The second method, Text2Live, is a recent text-guided video editing method. The third method, SDEdit, adds noise to each input frame and denoises it based on the edit prompt. The team has demonstrated how Pix2Video strikes a good balance between respecting the edit and keeping temporal consistency without requiring training. It outperforms the baseline methods regarding temporal coherency, CLIP-Image score, and Pixel-MSE. In conclusion. Pix2Video is an innovative approach for text-guided video editing and seems promising.
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Tanya Malhotra is a final year undergrad from the University of Petroleum Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.