Effectively extracting inter-frame motion and appearance information is important for video
frame interpolation (VFI). Previous works either extract both types of information in a mixed …

We present an approach to modeling an image-space prior on scene motion. Our prior is
learned from a collection of motion trajectories extracted from real video sequences …

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to
previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently …

Abstract We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for
video frame interpolation. It is based on two essential designs. First, we build bidirectional …

Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Animating a still image offers an engaging visual experience. Traditional image animation
techniques mainly focus on animating natural scenes with stochastic dynamics (eg clouds …

A central challenge of video prediction lies where the system has to reason the object's
future motion from image frames while simultaneously maintaining the consistency of its …

With the impressive progress in diffusion-based text-to-image generation, extending such
powerful generative ability to text-to-video raises enormous attention. Existing methods …

Abstract Combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs)
with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded …

In this paper we present a few-shot text-to-video framework LAMP which enables a text-to-
image diffusion model to Learn A specific Motion Pattern with 8 16 videos on a single GPU …