235 research outputs found
Template-based Monocular 3D Shape Recovery using Laplacian Meshes
We show that by extending the Laplacian formalism, which was first introduced in the Graphics community to regularize 3D meshes, we can turn the monocular 3D shape reconstruction of a deformable surface given correspondences with a reference image into a well-posed problem. Our approach allows us to reduce the dimensionality of the surface reconstruction problem without sacrificing accuracy, thus allowing for real-time implementations
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
We propose a method for predicting the 3D shape of a deformable surface from
a single view. By contrast with previous approaches, we do not need a
pre-registered template of the surface, and our method is robust to the lack of
texture and partial occlusions. At the core of our approach is a {\it
geometry-aware} deep architecture that tackles the problem as usually done in
analytic solutions: first perform 2D detection of the mesh and then estimate a
3D shape that is geometrically consistent with the image. We train this
architecture in an end-to-end manner using a large dataset of synthetic
renderings of shapes under different levels of deformation, material
properties, textures and lighting conditions. We evaluate our approach on a
test split of this dataset and available real benchmarks, consistently
improving state-of-the-art solutions with a significantly lower computational
time.Comment: Accepted at CVPR 201
Learning quadrangulated patches for 3D shape parameterization and completion
We propose a novel 3D shape parameterization by surface patches, that are
oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail
on local patches, we learn a patch dictionary that identifies principal surface
features of the shape. Unlike previous methods, we are able to encode surface
patches of variable size as determined by the user. We propose novel methods
for dictionary learning and patch reconstruction based on the query of a noisy
input patch with holes. We evaluate the patch dictionary towards various
applications in 3D shape inpainting, denoising and compression. Our method is
able to predict missing vertices and inpaint moderately sized holes. We
demonstrate a complete pipeline for reconstructing the 3D mesh from the patch
encoding. We validate our shape parameterization and reconstruction methods on
both synthetic shapes and real world scans. We show that our patch dictionary
performs successful shape completion of complicated surface textures.Comment: To be presented at International Conference on 3D Vision 2017, 201
{HiFECap}: {M}onocular High-Fidelity and Expressive Capture of Human Performances
Monocular 3D human performance capture is indispensable for many applicationsin computer graphics and vision for enabling immersive experiences. However,detailed capture of humans requires tracking of multiple aspects, including theskeletal pose, the dynamic surface, which includes clothing, hand gestures aswell as facial expressions. No existing monocular method allows joint trackingof all these components. To this end, we propose HiFECap, a new neural humanperformance capture approach, which simultaneously captures human pose,clothing, facial expression, and hands just from a single RGB video. Wedemonstrate that our proposed network architecture, the carefully designedtraining strategy, and the tight integration of parametric face and hand modelsto a template mesh enable the capture of all these individual aspects.Importantly, our method also captures high-frequency details, such as deformingwrinkles on the clothes, better than the previous works. Furthermore, we showthat HiFECap outperforms the state-of-the-art human performance captureapproaches qualitatively and quantitatively while for the first time capturingall aspects of the human.<br
Deep deformable models for 3D human body
Deformable models are powerful tools for modelling the 3D shape variations for a class of objects. However, currently the application and performance of deformable models for human body are restricted due to the limitations in current 3D datasets, annotations, and the model formulation itself. In this thesis, we address the issue by making the following contributions in the field of 3D human body modelling, monocular reconstruction and data collection/annotation.
Firstly, we propose a deep mesh convolutional network based deformable model for 3D human body. We demonstrate the merit of this model in the task of monocular human mesh recovery. While outperforming current state of the art models in mesh recovery accuracy, the model is also light weighted and more flexible as it can be trained end-to-end and fine-tuned for a specific task.
A second contribution is a bone level skinned model of 3D human mesh, in which bone modelling and identity-specific variation modelling are decoupled. Such formulation allows the use of mesh convolutional networks for capturing detailed identity specific variations, while explicitly controlling and modelling the pose variations through linear blend skinning with built-in motion constraints. This formulation not only significantly increases the accuracy in 3D human mesh reconstruction, but also facilitates accurate in the wild character animation and retargetting.
Finally we present a large scale dataset of over 1.3 million 3D human body scans in daily clothing. The dataset contains over 12 hours of 4D recordings at 30 FPS, consisting of 7566 dynamic sequences of 3D meshes from 4205 subjects. We propose a fast and accurate sequence registration pipeline which facilitates markerless motion capture and automatic dense annotation for the raw scans, leading to automatic synthetic image and annotation generation that boosts the performance for tasks such as monocular human mesh reconstruction.Open Acces
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
Shape basis interpretation for monocular deformable 3D reconstruction
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft
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