We propose a 3D compositional morphable model of eyeglasses that accurately incorporates high-fidelity geometric and photometric interaction effects.

We employ a hybrid representation that combines surface geometry and a volumetric representation to enable modification of geometry, lens insertion and frame deformation.

Our model is relightable under point lights and natural illumination, which can synthesize casting shadows between faces and glasses

We propose a practical photometric solution for the in-the-wild inverse rendering under unknown ambient lighting.

We recovers scene geometry and reflectance using only multi-view images captured by a smartphone.

The key idea is to exploit smartphone’s built-in flashlight as a minimally controlled light source, and decompose images into two photometric components: a static appearance corresponds to ambient flux, plus a dynamic reflection induced by the flashlight.

Introduced a self-supervised neural network for uncalibrated photometric stereo problem.

The object surface shape, and light sources are jointly estimated via the neural network in an unsupervised manner

Formulated the shape estimation and material estimation in a self-supervised framework. Explicitly predicted shadows to mitigate the errors.

Achieved the state-of-the-art performance in surface normal estimation and been an order of magnitude faster than previous methods.

The proposed neural representation of reflectance also presents higher quality in object relighting task than prior works.

Proposed a neural representation for the plenoptic function, which describes light rays observed from any given position in every viewing direction.

Proposed proxy depth reconstruction and color-blending network for achieving well approximation on the complete plenoptic function.

The generated results are in high-quality with better PSNR than previous methods. The training and testing time of proposed method is also more than 10 times faster than prior works.

Dramatically decrease the demands on the photometric stereo problem by reducing the number of images at input.

Automatically learn the critical and informative illuminations required at input.

A frequency domain neural network for image super-resolution.

Employs the convolution theorem so as to cast convolutions in the spatial domain as products in the frequency domain.

The network is very computationally efficient at testing, which is one to two orders of magnitude faster than the previous works.

A deep network for images super-resolution with stereo images at input. The network is designed to allow combining structural information in the image across large regions efficiently.

By learning the residual image, the network copes better with vanishing gradients and its devoid of gradient clipping operations.