M
Mohammad Shafiei
Researcher at Max Planck Society
Publications - 10
Citations - 1883
Mohammad Shafiei is an academic researcher from Max Planck Society. The author has contributed to research in topics: Pose & Frame rate. The author has an hindex of 6, co-authored 10 publications receiving 1374 citations. Previous affiliations of Mohammad Shafiei include University of California, San Diego.
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Journal ArticleDOI
VNect: real-time 3D human pose estimation with a single RGB camera
Dushyant Mehta,Srinath Sridhar,Oleksandr Sotnychenko,Helge Rhodin,Mohammad Shafiei,Hans-Peter Seidel,Weipeng Xu,Dan Casas,Christian Theobalt +8 more
TL;DR: In this paper, a fully-convolutional pose formulation was proposed to regress 2D and 3D joint positions jointly in real-time and does not require tightly cropped input frames.
Journal ArticleDOI
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
Dushyant Mehta,Srinath Sridhar,Oleksandr Sotnychenko,Helge Rhodin,Mohammad Shafiei,Hans-Peter Seidel,Weipeng Xu,Dan Casas,Christian Theobalt +8 more
TL;DR: This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.
Journal ArticleDOI
EgoCap: egocentric marker-less motion capture with two fisheye cameras
Helge Rhodin,Christian Richardt,Dan Casas,Eldar Insafutdinov,Mohammad Shafiei,Hans-Peter Seidel,Bernt Schiele,Christian Theobalt +7 more
TL;DR: In this article, the authors estimate the full-body skeleton pose from a lightweight stereo pair of fisheye cameras attached to a helmet or virtual reality headset, which can be used in indoor and outdoor scenes.
Proceedings ArticleDOI
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image
TL;DR: In this article, a deep inverse rendering framework is proposed to estimate shape, lighting, and surface reflectance from a single RGB image of an arbitrary indoor scene, and a cascade structure is used to iteratively refine the predictions.
Posted Content
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image
TL;DR: A deep inverse rendering framework for indoor scenes, which combines novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset.