Video Editing

Follow Your Pose can generate character videos that match specific poses from text descriptions. It uses a two-stage training process with pre-trained text-to-image models, allowing for continuous pose control and editing.

vid2vid-zero can edit videos without needing extra training on video data. It uses image diffusion models for text-to-video alignment and keeps the original video’s look and feel, allowing for effective changes to scenes and subjects.

Blind Video Deflickering by Neural Filtering with a Flawed Atlas can remove flicker from videos without needing extra guidance. It works well on different types of videos and uses a neural atlas for better consistency, outperforming other methods.

Video-P2P can edit videos using advanced techniques like word swap and prompt refinement. It adapts image generation models for video, allowing for the creation of new characters while keeping original poses and scenes.

Dreamix can edit videos based on a text prompt while keeping colors, sizes, and camera angles consistent. It combines low-resolution video data with high-quality content, allowing for advanced editing of motion and appearance.

Shape-aware Text-driven Layered Video Editing can edit the shape of objects in videos while keeping them consistent across frames. It uses a text-conditioned diffusion model to achieve this, making video editing more effective than other methods.

MAGVIT can perform video synthesis tasks like inpainting, outpainting, and generating animations from single images. It is much faster than other models, working 100 times quicker than diffusion models and 60 times faster than autoregressive models, while also achieving the best results on multiple benchmarks.

VToonify can create high-quality artistic portrait videos from images. It allows for controllable style transfer on non-aligned faces and produces smooth, coherent videos with flexible controls on color and intensity.