Recent improvements in synthetic data generation make it possible to produce images that are highly photorealistic and indistinguishable from real ones.
The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space.
Ranked #1 on Scene Graph Classification on Visual Genome (R@20 metric)
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario.
We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware.
We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on 3D Facial Expression Recognition on 2017_test set (using extra training data)
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on Facial Landmark Detection on 300W (Mean Error Rate metric)
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition.
We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan.
The 3D shapes of faces are well known to be discriminative.
Ranked #4 on 3D Face Reconstruction on Florence (Average 3D Error metric)
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian.
We propose a novel approach to template based face recognition.
We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors.
Face recognition capabilities have recently made extraordinary leaps.
Ranked #12 on Face Verification on IJB-A
no code implementations • 23 Mar 2016 • Wael Abd-Almageed, Yue Wua, Stephen Rawlsa, Shai Harel, Tal Hassner, Iacopo Masi, Jongmoo Choi, Jatuporn Toy Leksut, Jungyeon Kim, Prem Natarajan, Ram Nevatia, Gerard Medioni
In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features.
Ranked #14 on Face Verification on IJB-A
We prove a closed-form solution to tensor voting (CFTV): given a point set in any dimensions, our closed-form solution provides an exact, continuous and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation.
We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression.
We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization.