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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…
Resolving 3D Human Pose Ambiguities with 3D Scene Constraints
Mohamed Hassan, Vasileios Choutas, Dimitrios Tzionas +1
To understand and analyze human behavior, we need to capture humans moving in, and interacting with, the world. Most existing methods perform 3D human pose estimation without expli…
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…
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani +4
To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To…
Learning joint reconstruction of hands and manipulated objects
Yana Hasson, Gül Varol, Dimitrios Tzionas +4
Estimating hand-object manipulations is essential for interpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and…