49 research outputs found
Multi-frame scene-flow estimation using a patch model and smooth motion prior
This paper addresses the problem of estimating the dense 3D motion of a scene over several frames using a set of calibrated cameras. Most current 3D motion estimation techniques are limited to estimating the motion over a single frame, unless a strong prior model of the scene (such as a skeleton) is introduced. Estimating the 3D motion of a general scene is difficult due to untextured surfaces, complex movements and occlusions. In this paper, we show that it is possible to track the surfaces of a scene over several frames, by introducing an effective prior on the scene motion. Experimental results show that the proposed method estimates the dense scene-flow over multiple frames, without the need for multiple-view reconstructions at every frame. Furthermore, the accuracy of the proposed method is demonstrated by comparing the estimated motion against a ground truth
RECREATING AND SIMULATING DIGITAL COSTUMES FROM A STAGE PRODUCTION OF \u3ci\u3eMEDEA\u3c/i\u3e
This thesis investigates a technique to effectively construct and simulate costumes from a stage production Medea, in a dynamic cloth simulation application like Maya\u27s nDynamics. This was done by using data collected from real-world fabric tests and costume construction in the theatre\u27s costume studio. Fabric tests were conducted and recorded, by testing costume fabrics for drape and behavior with two collision objects. These tests were recreated digitally in Maya to derive appropriate parameters for the digital fabric, by comparing with the original reference. Basic mannequin models were created using the actors\u27 measurements and skeleton-rigged to enable animation. The costumes were then modeled and constrained according to the construction process observed in the costume studio to achieve the same style and stitch as the real costumes. Scenes selected and recorded from Medea were used as reference to animate the actors\u27 models. The costumes were assigned the parameters derived from the fabric tests to produce the simulations. Finally, the scenes were lit and rendered out to obtain the final videos which were compared to the original recordings to ascertain the accuracy of simulation. By obtaining and refining simulation parameters from simple fabric collision tests, and modeling the digital costumes following the procedures derived from real-life costume construction, realistic costume simulation was achieved
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
-minimization algorithm by adaptively estimating the value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image
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MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
While models of 3D clothing learned from real data exist, no method can
predict clothing deformation as a function of garment size. In this paper, we
introduce SizerNet to predict 3D clothing conditioned on human body shape and
garment size parameters, and ParserNet to infer garment meshes and shape under
clothing with personal details in a single pass from an input mesh. SizerNet
allows to estimate and visualize the dressing effect of a garment in various
sizes, and ParserNet allows to edit clothing of an input mesh directly,
removing the need for scan segmentation, which is a challenging problem in
itself. To learn these models, we introduce the SIZER dataset of clothing size
variation which includes different subjects wearing casual clothing items
in various sizes, totaling to approximately 2000 scans. This dataset includes
the scans, registrations to the SMPL model, scans segmented in clothing parts,
garment category and size labels. Our experiments show better parsing accuracy
and size prediction than baseline methods trained on SIZER. The code, model and
dataset will be released for research purposes.Comment: European Conference on Computer Vision 202