Marcus Magnor

Bio: Marcus Magnor is an academic researcher from Braunschweig University of Technology. The author has contributed to research in topics: Rendering (computer graphics) & Optical flow. The author has an hindex of 18, co-authored 35 publications receiving 951 citations. Previous affiliations of Marcus Magnor include Max Planck Society.

Detailed Human Avatars from Monocular Video

Abstract: We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture. Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date. Numerous results show the robustness and versatility of our method. A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference.

Image-based tomographic reconstruction of flames

Abstract: Non-invasively determining the three-dimensional structure of real flames is a challenging task. We present a tomographic method for reconstructing a volumetric model from multiple images of fire. The method is similar to sparse-view computerized tomography and is applicable to static camera setups observing dynamic flames. Using an algebraic reconstruction method, we can restrict the solution space such that a high quality model is obtained from only a small number of camera images. An additional advantage is fast processing of multi-video sequences to generate time-varying models for animation purposes. The resulting three-dimensional fire model is useful for realistic rendering of fire animations, as well as for analyzing gasdynamics of fires.

Image-based tomographic reconstruction of flames

Abstract: Non-invasively determining the three-dimensional structure of real flames is a challenging task. We present a tomographic method for reconstructing a volumetric model from multiple images of fire. The method is similar to sparse-view computerized tomography and is applicable to static camera setups observing dynamic flames. Using an algebraic reconstruction method, we can restrict the solution space such that a high quality model is obtained from only a small number of camera images. An additional advantage is fast processing of multi-video sequences to generate time-varying models for animation purposes. The resulting three-dimensional fire model is useful for realistic rendering of fire animations, as well as for analyzing gasdynamics of fires.

Combining 2d feature tracking and volume reconstruction for online video-based human motion capture

Abstract: The acquisition of human motion data is of major importance for creating interactive virtual environments, intelligent user interfaces, and realistic computer animations. Today's performance of off-the-shelf computer hardware enables marker-free non-intrusive optical tracking of the human body. In addition, recent research shows that it is possible to efficiently acquire and render volumetric scene representations in real-time. This paper describes a system to capture human motion without the use of markers or scene-intruding devices. Instead, a 2D feature tracking algorithm and a silhouette-based 3D volumetric scene reconstruction method are applied directly to the image data. A person is recorded by multiple synchronized cameras, and a multi-layer hierarchical kinematic skeleton is fitted to each frame in a two-stage process. The pose of a first model layer at every time step is determined from the tracked 3D locations of hands, head and feet. A more sophisticated second skeleton layer is fitted to the motion data by applying a volume registration technique. We present results with a prototype system showing that the approach is capable of running at interactive frame rates.

Perception-motivated interpolation of image sequences

Abstract: We present a method for image interpolation that is able to create high-quality, perceptually convincing transitions between recorded images. By implementing concepts derived from human vision, the problem of a physically correct image interpolation is relaxed to that of image interpolation which is perceived as visually correct by human observers. We find that it suffices to focus on exact edge correspondences, homogeneous regions and coherent motion to compute convincing results. A user study confirms the visual quality of the proposed image interpolation approach. We show how each aspect of our approach increases perceived quality of the result. We compare the results to other methods and assess achievable quality for different types of scenes.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

Abstract: Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

Abstract: We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

Free-viewpoint video of human actors

Abstract: In free-viewpoint video, the viewer can interactively choose his viewpoint in 3-D space to observe the action of a dynamic real-world scene from arbitrary perspectives. The human body and its motion plays a central role in most visual media and its structure can be exploited for robust motion estimation and efficient visualization. This paper describes a system that uses multi-view synchronized video footage of an actor's performance to estimate motion parameters and to interactively re-render the actor's appearance from any viewpoint.The actor's silhouettes are extracted from synchronized video frames via background segmentation and then used to determine a sequence of poses for a 3D human body model. By employing multi-view texturing during rendering, time-dependent changes in the body surface are reproduced in high detail. The motion capture subsystem runs offline, is non-intrusive, yields robust motion parameter estimates, and can cope with a broad range of motion. The rendering subsystem runs at real-time frame rates using ubiquous graphics hardware, yielding a highly naturalistic impression of the actor. The actor can be placed in virtual environments to create composite dynamic scenes. Free-viewpoint video allows the creation of camera fly-throughs or viewing the action interactively from arbitrary perspectives.