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Dreamix: A New Diffusion Model for Video Editing

The model is capable of generating and editing videos from image and text prompts.

A team of AI developers has unveiled Dreamix, a new method for text-based motion and appearance editing of videos. The approach, which is the first diffusion-based method of its kind, combines low-resolution spatio-temporal information from the original video with newly synthesized high-resolution information to align with a guiding text prompt, allowing one to create videos based on image and text inputs. 

To improve the motion editability, the team has also proposed a mixed objective that jointly fine-tunes with full temporal attention and temporal attention masking. The developers have also introduced a new framework for image animation, which transforms an image into a coarse video through simple image processing operations and then uses the general video editor to animate it.

Our method supports multiple applications by application-dependent pre-processing, converting the input content into a uniform video format. For image-to-video, the input image is duplicated and transformed using perspective transformations, synthesizing a coarse video with some camera motion. For subject-driven video generation, the input is omitted - finetuning alone takes care of fidelity," commented the team. "This coarse video is then edited using our general Dreamix Video Editor: we first corrupt the video by downsampling followed by adding noise. We then apply the finetuned text-guided video diffusion model, which upscales the video to the final spatio-temporal resolution"

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