15 papers with code • 0 benchmarks • 0 datasets
Image Animation is a field for image-animation of a source image by a driving video
These leaderboards are used to track progress in Image Animation
Most implemented papers
First Order Motion Model for Image Animation
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Image Animation with Perturbed Masks
We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object.
Animating Arbitrary Objects via Deep Motion Transfer
This is achieved through a deep architecture that decouples appearance and motion information.
Creative Flow+ Dataset
We present the Creative Flow+ Dataset, the first diverse multi-style artistic video dataset richly labeled with per-pixel optical flow, occlusions, correspondences, segmentation labels, normals, and depth.
Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation
We show that our framework can spatially transform the inputs in an efficient manner.
Ultra-low bitrate video conferencing using deep image animation
In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications.
PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation
State-of-the-art self-supervised image animation approaches warp the source image according to the motion of the driving video and recover the warping artifacts by inpainting.
DeepFake MNIST+: A DeepFake Facial Animation Dataset
It includes 10, 000 facial animation videos in ten different actions, which can spoof the recent liveness detectors.
Latent Image Animator: Learning to animate image via latent space navigation
Deviating from such models, we here introduce Latent Image Animator (LIA), a self-supervised auto-encoder that evades need for structure representation.
Neural Fields in Visual Computing and Beyond
Recent advances in machine learning have created increasing interest in solving visual computing problems using a class of coordinate-based neural networks that parametrize physical properties of scenes or objects across space and time.