Image-to-Video

ConsistI2V is an image-to-video method with enhanced visual consistency. Compared to other methods, this one is able to better maintain the subject, background, and style from the first frame, as well as ensure a fluid and logical progression while supporting long video generation as well as camera motion control.

Last year we got real-time diffusion for images, this year we’ll get it for video! AnimateLCM can generate high-fidelity videos with minimal steps. The model also supports image-to-video as well as support for adapters like ControlNet. It’s not available yet, but once it hits, expect way more AI generated video content.

Motion-I2V can generate videos from images with clear and controlled motion. It uses a two-stage process with a motion field predictor and temporal attention, allowing for precise control over how things move and enabling video-to-video translation without needing extra training.

LiveSketch can automatically add motion to a single-subject sketch by providing a text prompt indicating the desired motion. The output are short SVG animations which can be easily edited.

I2VGen-XL can generate high-quality videos from static images using a cascaded diffusion model. It achieves a resolution of 1280x720 and improves the flow of movement in videos through a two-stage process that separates detail enhancement from overall coherence.

DreamPose can generate animated fashion videos from a single image and a sequence of human body poses. The method is able to capture both human and fabric motion and supports a variety of clothing styles and poses.

3D Cinemagraphy can turn a single still image into a video by adding motion and depth. It uses 3D space to create realistic animations and fix common issues like artifacts and inconsistent movements.