Existing diffusion-based video editing models have made gorgeous advances for editing
attributes of a source video over time but struggle to manipulate the motion information while …

We propose RANA, a relightable and articulated neural avatar for the photorealistic
synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a …

Image and video synthesis has become a blooming topic in computer vision and machine
learning communities along with the developments of deep generative models, due to its …

Image animation aims to animate a source image by using motion learned from a driving
video. Current state-of-the-art methods typically use convolutional neural networks (CNNs) …

Motion transfer aims to transfer the motion of a driving video to a source image. When there
are considerable differences between object in the driving video and that in the source …

Human motion transfer aims to transfer motions from a target dynamic person to a source
static one for motion synthesis. An accurate matching between the source person and the …

One compelling application of artificial intelligence is to generate a video of a target person
performing arbitrary desired motion (from a source person). While the state-of-the-art …

Numerous pose-guided human editing methods have been explored by the vision
community due to their extensive practical applications. However, most of these methods still …

Human motion copy is an intriguing yet challenging task in artificial intelligence and
computer vision, which strives to generate a fake video of a target person performing the …

Despite impressive advancements in diffusion-based video editing models in altering video
attributes, there has been limited exploration into modifying motion information while …