416 citations · 2.6k across the 36 of their papers we have counts for
38 papers
Attribute-conditioned Layout GAN for Automatic Graphic Design
Jianan Li, Jimei Yang, Jianming Zhang +3
Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (…
Contact and Human Dynamics from Monocular Video
Davis Rempe, Leonidas J. Guibas, Aaron Hertzmann +3
Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetr…
Generative Tweening: Long-term Inbetweening of 3D Human Motions
Yi Zhou, Jingwan Lu, Connelly Barnes +3
The ability to generate complex and realistic human body animations at scale, while following specific artistic constraints, has been a fundamental goal for the game and animation…
High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
Yu Zeng, Zhe Lin, Jimei Yang +3
Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. To address this challenge, we propose an iterative inpainting method w…
FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images
Christian Zimmermann, Duygu Ceylan, Jimei Yang +3
Estimating 3D hand pose from single RGB images is a highly ambiguous problem that relies on an unbiased training dataset. In this paper, we analyze cross-dataset generalization whe…
3D Ken Burns Effect from a Single Image
Simon Niklaus, Long Mai, Jimei Yang +1
The Ken Burns effect allows animating still images with a virtual camera scan and zoom. Adding parallax, which results in the 3D Ken Burns effect, enables significantly more compel…
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control
Yu-Wei Chao, Jimei Yang, Weifeng Chen +1
Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement lear…
Multimodal Style Transfer via Graph Cuts
Yulun Zhang, Chen Fang, Yilin Wang +4
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alterna…
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Jianan Li, Jimei Yang, Aaron Hertzmann +2
Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric rel…
Foreground-aware Image Inpainting
Wei Xiong, Jiahui Yu, Zhe Lin +4
Existing image inpainting methods typically fill holes by borrowing information from surrounding pixels. They often produce unsatisfactory results when the holes overlap with or to…
On the Continuity of Rotation Representations in Neural Networks
Yi Zhou, Connelly Barnes, Jingwan Lu +2
In neural networks, it is often desirable to work with various representations of the same space. For example, 3D rotations can be represented with quaternions or Euler angles. In…
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Tao Zhou, Chen Fang, Zhaowen Wang +5
Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simula…
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Yijun Li, Chen Fang, Jimei Yang +3
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame. In this work, we study the problem of generating co…
Free-Form Image Inpainting with Gated Convolution
Jiahui Yu, Zhe Lin, Jimei Yang +3
We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images witho…
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
Chen Liu, Jimei Yang, Duygu Ceylan +2
This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image d…
Neural Kinematic Networks for Unsupervised Motion Retargetting
Ruben Villegas, Jimei Yang, Duygu Ceylan +1
We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting.…
BodyNet: Volumetric Inference of 3D Human Body Shapes
Gül Varol, Duygu Ceylan, Bryan Russell +4
Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging du…
MAttNet: Modular Attention Network for Referring Expression Comprehension
Licheng Yu, Zhe Lin, Xiaohui Shen +4
In this paper, we address referring expression comprehension: localizing an image region described by a natural language expression. While most recent work treats expressions as a…
Generative Image Inpainting with Contextual Attention
Jiahui Yu, Zhe Lin, Jimei Yang +3
Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually pla…
Predicting Scene Parsing and Motion Dynamics in the Future
Xiaojie Jin, Huaxin Xiao, Xiaohui Shen +6
The ability of predicting the future is important for intelligent systems, e.g. autonomous vehicles and robots to plan early and make decisions accordingly. Future scene parsing an…
Material Editing Using a Physically Based Rendering Network
Guilin Liu, Duygu Ceylan, Ersin Yumer +2
The ability to edit materials of objects in images is desirable by many content creators. However, this is an extremely challenging task as it requires to disentangle intrinsic phy…
FoveaNet: Perspective-aware Urban Scene Parsing
Xin Li, Zequn Jie, Wei Wang +7
Parsing urban scene images benefits many applications, especially self-driving. Most of the current solutions employ generic image parsing models that treat all scales and location…
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks
Chuhang Zou, Ersin Yumer, Jimei Yang +2
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sen…
Deep GrabCut for Object Selection
Ning Xu, Brian Price, Scott Cohen +2
Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large…