J
Jimei Yang
Researcher at Adobe Systems
Publications - 145
Citations - 18302
Jimei Yang is an academic researcher from Adobe Systems. The author has contributed to research in topics: Rendering (computer graphics) & Computer science. The author has an hindex of 52, co-authored 136 publications receiving 13213 citations. Previous affiliations of Jimei Yang include Chinese Academy of Sciences & University of California, Merced.
Papers
More filters
Patent
Recognizing combinations of body shape, pose, and clothing in three-dimensional input images
TL;DR: In this article, a machine learning algorithm is trained to recognize the pose-shape-clothing combinations in the synthetic training images and to generate feature descriptors describing the pose shape-clothes combinations.
Posted Content
Material Editing Using a Physically Based Rendering Network
TL;DR: This work proposes an end-to-end network architecture that replicates the forward image formation process to accomplish material editing, and demonstrates a rich set of visually plausible material editing examples and provides an extensive comparative study.
Posted Content
Neural Kinematic Networks for Unsupervised Motion Retargetting
TL;DR: In this article, a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective is proposed for unsupervised motion retargeting, which works online and adapts the motion sequence on-the-fly as new frames are received.
Posted Content
FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images
TL;DR: In this article, a large-scale multi-view hand dataset with both 3D hand pose and shape annotations is introduced, and an iterative, semi-automated human-in-the-loop approach is proposed.
Posted Content
Forecasting Human Dynamics from Static Images
TL;DR: Wang et al. as discussed by the authors proposed the 3D Pose Forecasting Network (3D-PFNet), which combines single-image human pose estimation and sequence prediction, and converts the 2D predictions into 3D space.