Search Results for author:
Found 8 papers, 3 papers with code
S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image
We present a novel face reconstruction method capable of reconstructing detailed face geometry, spatially varying face reflectance from a single monocular image.
Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing
In this paper, we build our work on the aforementioned approaches and propose a new method that greatly improves reconstruction quality and robustness in general scenes.
Ranked #12 on
3D Face Reconstruction
on NoW Benchmark
PhotoApp: Photorealistic Appearance Editing of Head Portraits
We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination in a portrait image.
Practical Face Reconstruction via Differentiable Ray Tracing
The proposed method models scene illumination via a novel, parameterized virtual light stage, which in-conjunction with differentiable ray-tracing, introduces a coarse-to-fine optimization formulation for face reconstruction.
Face Reflectance and Geometry Modeling via Differentiable Ray Tracing
We present a novel strategy to automatically reconstruct 3D faces from monocular images with explicitly disentangled facial geometry (pose, identity and expression), reflectance (diffuse and specular albedo), and self-shadows.
SoDeep: a Sorting Deep net to learn ranking loss surrogates
Our approach is based on a deep architecture that approximates the sorting of arbitrary sets of scores.
Finding beans in burgers: Deep semantic-visual embedding with localization
Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic relationships.
Hybrid multi-layer Deep CNN/Aggregator feature for image classification
A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.
