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Found 15 papers, 7 papers with code
Prompt-to-Prompt Image Editing with Cross Attention Control
Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome.
MoDi: Unconditional Motion Synthesis from Diverse Data
Our model is trained in a completely unsupervised setting from a diverse, unstructured and unlabeled motion dataset and yields a well-behaved, highly semantic latent space.
GANimator: Neural Motion Synthesis from a Single Sequence
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence.
MyStyle: A Personalized Generative Prior
Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre.
Deep Saliency Prior for Reducing Visual Distraction
Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distraction in images.
Learning Skeletal Articulations with Neural Blend Shapes
Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning.
Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.
Neural Alignment for Face De-pixelization
Although simple, our framework synthesizes high-quality face reconstructions, demonstrating that given the statistical prior of a human face, multiple aligned pixelated frames contain sufficient information to reconstruct a high-quality approximation of the original signal.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
Skeleton-Aware Networks for Deep Motion Retargeting
In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.
Unpaired Motion Style Transfer from Video to Animation
In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.
Learning Character-Agnostic Motion for Motion Retargeting in 2D
In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.
Deep Video-Based Performance Cloning
After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances.
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications.
