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VIBE: Video Inference for Human Body Pose and Shape Estimation
Muhammed Kocabas, Nikos Athanasiou, Michael J. Black
Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce…
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…
Attacking Optical Flow
Anurag Ranjan, Joel Janai, Andreas Geiger +1
Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-drivin…
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 Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop
Nikos Kolotouros, Georgios Pavlakos, Michael J. Black +1
Model-based human pose estimation is currently approached through two different paradigms. Optimization-based methods fit a parametric body model to 2D observations in an iterative…
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"
Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf +1
We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing…