25,664 research outputs found
Combining Dense Nonrigid Structure from Motion and 3D Morphable Models for Monocular 4D Face Reconstruction
This is the author accepted manuscript. The final version is available from ACM via the DOI in this record Monocular 4D face reconstruction is a challenging problem, especially in the case that the input video is captured under unconstrained conditions, i.e. "in the wild". The majority of the state-of-the-art approaches build upon 3D Morphable Modelling (3DMM), which has been proven to be more robust than model-free approaches such as Shape from Shading (SfS) or Structure from Motion (SfM). While offering visually plausible shape reconstruction results that resemble real faces, 3DMMs adhere to the model space learned from exemplar faces during the training phase, often yielding facial reconstructions that are excessively smooth and look too similar even across captured faces with completely different facial characteristics. This is due to the fact that 3DMMs are typically used as hard constraints on the reconstructed 3D shape. To overcome these limitations, in this paper we propose to combine 3DMMs with Dense Nonrigid Structure from Motion (DNSM), which is much less robust but has the potential of reconstructing fine details and capturing the subject-specific facial characteristics of every input. We effectively combine the best of both worlds by introducing a novel dense variational framework, which we solve efficiently by designing a convex optimisation strategy. In contrast to previous methods, we incorporate 3DMM as a soft constraint, penalizing both departure of reconstructed faces from the 3DMM subspace and variation of the identity component of the 3DMM over different frames of the input video. As demonstrated in qualitative and quantitative experiments, our method is robust, accurately estimates the 3D facial shape over time and outperforms other state-of-the-art methods of 4D face reconstruction
Temporally coherent 4D reconstruction of complex dynamic scenes
This paper presents an approach for reconstruction of 4D temporally coherent
models of complex dynamic scenes. No prior knowledge is required of scene
structure or camera calibration allowing reconstruction from multiple moving
cameras. Sparse-to-dense temporal correspondence is integrated with joint
multi-view segmentation and reconstruction to obtain a complete 4D
representation of static and dynamic objects. Temporal coherence is exploited
to overcome visual ambiguities resulting in improved reconstruction of complex
scenes. Robust joint segmentation and reconstruction of dynamic objects is
achieved by introducing a geodesic star convexity constraint. Comparative
evaluation is performed on a variety of unstructured indoor and outdoor dynamic
scenes with hand-held cameras and multiple people. This demonstrates
reconstruction of complete temporally coherent 4D scene models with improved
nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 . Video available at:
https://www.youtube.com/watch?v=bm_P13_-Ds
Multi-body Non-rigid Structure-from-Motion
Conventional structure-from-motion (SFM) research is primarily concerned with
the 3D reconstruction of a single, rigidly moving object seen by a static
camera, or a static and rigid scene observed by a moving camera --in both cases
there are only one relative rigid motion involved. Recent progress have
extended SFM to the areas of {multi-body SFM} (where there are {multiple rigid}
relative motions in the scene), as well as {non-rigid SFM} (where there is a
single non-rigid, deformable object or scene). Along this line of thinking,
there is apparently a missing gap of "multi-body non-rigid SFM", in which the
task would be to jointly reconstruct and segment multiple 3D structures of the
multiple, non-rigid objects or deformable scenes from images. Such a multi-body
non-rigid scenario is common in reality (e.g. two persons shaking hands,
multi-person social event), and how to solve it represents a natural
{next-step} in SFM research. By leveraging recent results of subspace
clustering, this paper proposes, for the first time, an effective framework for
multi-body NRSFM, which simultaneously reconstructs and segments each 3D
trajectory into their respective low-dimensional subspace. Under our
formulation, 3D trajectories for each non-rigid structure can be well
approximated with a sparse affine combination of other 3D trajectories from the
same structure (self-expressiveness). We solve the resultant optimization with
the alternating direction method of multipliers (ADMM). We demonstrate the
efficacy of the proposed framework through extensive experiments on both
synthetic and real data sequences. Our method clearly outperforms other
alternative methods, such as first clustering the 2D feature tracks to groups
and then doing non-rigid reconstruction in each group or first conducting 3D
reconstruction by using single subspace assumption and then clustering the 3D
trajectories into groups.Comment: 21 pages, 16 figure
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