Search Results for author:
Found 23 papers, 11 papers with code
Flow Guided Transformable Bottleneck Networks for Motion Retargeting
Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person.
A Good Image Generator Is What You Need for High-Resolution Video Synthesis
We introduce a motion generator that discovers the desired trajectory, in which content and motion are disentangled.
Motion Representations for Articulated Animation
To facilitate animation and prevent the leakage of the shape of the driving object, we disentangle shape and pose of objects in the region space.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD (512)
InfinityGAN: Towards Infinite-Pixel Image Synthesis
We present a novel framework, InfinityGAN, for arbitrary-sized image generation.
In&Out : Diverse Image Outpainting via GAN Inversion
Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image.
SMIL: Multimodal Learning with Severely Missing Modality
A common assumption in multimodal learning is the completeness of training data, i. e., full modalities are available in all training examples.
Teachers Do More Than Teach: Compressing Image-to-Image Models
In this work, we aim to address these issues by introducing a teacher network that provides a search space in which efficient network architectures can be found, in addition to performing knowledge distillation.
Playable Video Generation
This paper introduces the unsupervised learning problem of playable video generation (PVG).
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
In this paper, we present MichiGAN (Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation method for interactive portrait hair manipulation.
Interactive Video Stylization Using Few-Shot Patch-Based Training
In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence.
Neural Hair Rendering
Unlike existing supervised translation methods that require model-level similarity to preserve consistent structure representation for both real images and fake renderings, our method adopts an unsupervised solution to work on arbitrary hair models.
Human Motion Transfer from Poses in the Wild
In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video.
Motion-supervised Co-Part Segmentation
To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.
First Order Motion Model for Image Animation
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD
Task-Assisted Domain Adaptation with Anchor Tasks
Our key idea to improve domain adaptation is to introduce a separate anchor task (such as facial landmarks) whose annotations can be obtained at no cost or are already available on both synthetic and real datasets.
Transformable Bottleneck Networks
We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).
Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering
Leveraging the assumption that learning the topic of a bug is a sub-task for detecting duplicates, we design a loss function that can jointly perform both tasks but needs supervision for only duplicate classification, achieving topic clustering in an unsupervised fashion.
3D Guided Fine-Grained Face Manipulation
This is achieved by first fitting a 3D face model and then disentangling the face into a texture and a shape.
Animating Arbitrary Objects via Deep Motion Transfer
This is achieved through a deep architecture that decouples appearance and motion information.
Hybrid VAE: Improving Deep Generative Models using Partial Observations
We show that such a combination is beneficial because the unlabeled data acts as a data-driven form of regularization, allowing generative models trained on few labeled samples to reach the performance of fully-supervised generative models trained on much larger datasets.
MoCoGAN: Decomposing Motion and Content for Video Generation
The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames.
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions
Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR).
Regressing a 3D Face Shape From a Single Image
To support the ability of our method to reliably reconstruct 3D shapes, we introduce a simple method for head pose estimation using a single image that reaches higher accuracy than the state of the art.
