36 research outputs found
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
We present an approach to matching images of objects in fine-grained datasets
without using part annotations, with an application to the challenging problem
of weakly supervised single-view reconstruction. This is in contrast to prior
works that require part annotations, since matching objects across class and
pose variations is challenging with appearance features alone. We overcome this
challenge through a novel deep learning architecture, WarpNet, that aligns an
object in one image with a different object in another. We exploit the
structure of the fine-grained dataset to create artificial data for training
this network in an unsupervised-discriminative learning approach. The output of
the network acts as a spatial prior that allows generalization at test time to
match real images across variations in appearance, viewpoint and articulation.
On the CUB-200-2011 dataset of bird categories, we improve the AP over an
appearance-only network by 13.6%. We further demonstrate that our WarpNet
matches, together with the structure of fine-grained datasets, allow
single-view reconstructions with quality comparable to using annotated point
correspondences.Comment: to appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild
We present SfSNet, an end-to-end learning framework for producing an accurate
decomposition of an unconstrained human face image into shape, reflectance and
illuminance. SfSNet is designed to reflect a physical lambertian rendering
model. SfSNet learns from a mixture of labeled synthetic and unlabeled real
world images. This allows the network to capture low frequency variations from
synthetic and high frequency details from real images through the photometric
reconstruction loss. SfSNet consists of a new decomposition architecture with
residual blocks that learns a complete separation of albedo and normal. This is
used along with the original image to predict lighting. SfSNet produces
significantly better quantitative and qualitative results than state-of-the-art
methods for inverse rendering and independent normal and illumination
estimation.Comment: Accepted to CVPR 2018 (Spotlight
End-to-end Recovery of Human Shape and Pose
We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In
contrast to most current methods that compute 2D or 3D joint locations, we
produce a richer and more useful mesh representation that is parameterized by
shape and 3D joint angles. The main objective is to minimize the reprojection
loss of keypoints, which allow our model to be trained using images in-the-wild
that only have ground truth 2D annotations. However, the reprojection loss
alone leaves the model highly under constrained. In this work we address this
problem by introducing an adversary trained to tell whether a human body
parameter is real or not using a large database of 3D human meshes. We show
that HMR can be trained with and without using any paired 2D-to-3D supervision.
We do not rely on intermediate 2D keypoint detections and infer 3D pose and
shape parameters directly from image pixels. Our model runs in real-time given
a bounding box containing the person. We demonstrate our approach on various
images in-the-wild and out-perform previous optimization based methods that
output 3D meshes and show competitive results on tasks such as 3D joint
location estimation and part segmentation.Comment: CVPR 2018, Project page with code: https://akanazawa.github.io/hmr