72 citations · 384 across the 36 of their papers we have counts for
6 papers · 1 filter
Generating 3D People in Scenes without People
Yan Zhang, Mohamed Hassan, Heiko Neumann +2
We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans…
Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video
Miao Liu, Siyu Tang, Yin Li +1
We address the challenging task of anticipating human-object interaction in first person videos. Most existing methods ignore how the camera wearer interacts with the objects, or s…
Learning Multi-Human Optical Flow
Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas +3
The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However…
Learning to Train with Synthetic Humans
David T. Hoffmann, Dimitrios Tzionas, Micheal J. Black +1
Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation…
Learning to Dress 3D People in Generative Clothing
Qianli Ma, Jinlong Yang, Anurag Ranjan +4
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not…
Frontal Low-rank Random Tensors for Fine-grained Action Segmentation
Yan Zhang, Krikamol Muandet, Qianli Ma +2
Fine-grained action segmentation in long untrimmed videos is an important task for many applications such as surveillance, robotics, and human-computer interaction. To understand s…