Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Insafutdinov, A."'
Autor:
Kästingschäfer, Marius, Gieruc, Théo, Bernhard, Sebastian, Campbell, Dylan, Insafutdinov, Eldar, Najafli, Eyvaz, Brox, Thomas
Models for egocentric 3D and 4D reconstruction, including few-shot interpolation and extrapolation settings, can benefit from having images from exocentric viewpoints as supervision signals. No existing dataset provides the necessary mixture of compl
Externí odkaz:
http://arxiv.org/abs/2412.00730
Autor:
Szymanowicz, Stanislaw, Insafutdinov, Eldar, Zheng, Chuanxia, Campbell, Dylan, Henriques, João F., Rupprecht, Christian, Vedaldi, Andrea
In this paper, we propose Flash3D, a method for scene reconstruction and novel view synthesis from a single image which is both very generalisable and efficient. For generalisability, we start from a "foundation" model for monocular depth estimation
Externí odkaz:
http://arxiv.org/abs/2406.04343
We present a method for the accurate 3D reconstruction of partly-symmetric objects. We build on the strengths of recent advances in neural reconstruction and rendering such as Neural Radiance Fields (NeRF). A major shortcoming of such approaches is t
Externí odkaz:
http://arxiv.org/abs/2206.06340
We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects. Because the LiDAR point clouds are partial, directly fitting bounding boxes to the point clouds is meaningle
Externí odkaz:
http://arxiv.org/abs/2109.07945
In this paper we predict a full 3D avatar of a person from a single image. We infer texture and geometry in the UV-space of the SMPL model using an image-to-image translation method. Given partial texture and segmentation layout maps derived from the
Externí odkaz:
http://arxiv.org/abs/1908.07117
We address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images. We train a convolutional network to predict both the shape and the pose from a single image by minimizing the reprojection e
Externí odkaz:
http://arxiv.org/abs/1810.09381
Autor:
Andriluka, Mykhaylo, Iqbal, Umar, Insafutdinov, Eldar, Pishchulin, Leonid, Milan, Anton, Gall, Juergen, Schiele, Bernt
Human poses and motions are important cues for analysis of videos with people and there is strong evidence that representations based on body pose are highly effective for a variety of tasks such as activity recognition, content retrieval and social
Externí odkaz:
http://arxiv.org/abs/1710.10000
Autor:
Rhodin, Helge, Richardt, Christian, Casas, Dan, Insafutdinov, Eldar, Shafiei, Mohammad, Seidel, Hans-Peter, Schiele, Bernt, Theobalt, Christian
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center. They often create discomfort by possibly needed marker suits, and their rec
Externí odkaz:
http://arxiv.org/abs/1701.00142
Autor:
Insafutdinov, Eldar, Andriluka, Mykhaylo, Pishchulin, Leonid, Tang, Siyu, Levinkov, Evgeny, Andres, Bjoern, Schiele, Bernt
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for single-frame pose estimation but is substantially faster. We achieve thi
Externí odkaz:
http://arxiv.org/abs/1612.01465
Autor:
Levinkov, Evgeny, Uhrig, Jonas, Tang, Siyu, Omran, Mohamed, Insafutdinov, Eldar, Kirillov, Alexander, Rother, Carsten, Brox, Thomas, Schiele, Bernt, Andres, Bjoern
We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including ins
Externí odkaz:
http://arxiv.org/abs/1611.04399