3D Shape Reconstruction
44 papers with code • 2 benchmarks • 4 datasets
We present Multi-Garment Network (MGN), a method to predict body shape and clothing, layered on top of the SMPL model from a few frames (1-8) of a video.
While the low-frequency component is predicted from pose, shape and style parameters with an MLP, the high-frequency component is predicted with a mixture of shape-style specific pose models.
The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints.
We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll.
Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.
Multi-modal 3D Shape Reconstruction Under Calibration Uncertainty using Parametric Level Set Methods
This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters.
This is challenging as it requires a model to learn a representation that can infer both the visible and occluded portions of any object using a limited training set.
3D human shape and pose estimation from monocular images has been an active area of research in computer vision, having a substantial impact on the development of new applications, from activity recognition to creating virtual avatars.