Search Results for author:
Found 26 papers, 6 papers with code
Model-Based Imitation Learning for Urban Driving
Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.
3D face reconstruction with dense landmarks
By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild.
Fake It Till You Make It: Face analysis in the wild using synthetic data alone
We demonstrate that it is possible to perform face-related computer vision in the wild using synthetic data alone.
Ranked #2 on
Face Parsing
on Helen
(using extra training data)
FastNeRF: High-Fidelity Neural Rendering at 200FPS
Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.
Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations
The increased availability and maturity of head-mounted and wearable devices opens up opportunities for remote communication and collaboration.
A high fidelity synthetic face framework for computer vision
Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others.
The Phong Surface: Efficient 3D Model Fitting using Lifted Optimization
Realtime perceptual and interaction capabilities in mixed reality require a range of 3D tracking problems to be solved at low latency on resource-constrained hardware such as head-mounted devices.
High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images
In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data.
CONFIG: Controllable Neural Face Image Generation
Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind.
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation.
DSAC - Differentiable RANSAC for Camera Localization
The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.
Fits Like a Glove: Rapid and Reliable Hand Shape Personalization
We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.
Decision Forests, Convolutional Networks and the Models in-Between
We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.
Model-Based Tracking at 300Hz Using Raw Time-of-Flight Observations
In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera.
Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose
In this paper, we show that we can significantly improving upon black box optimization by exploiting high-level knowledge of the structure of the parameters and using a local surrogate energy function.
Depth-Based Hand Pose Estimation: Data, Methods, and Challenges
To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
Training CNNs with Low-Rank Filters for Efficient Image Classification
Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.
Exploiting Uncertainty in Regression Forests for Accurate Camera Relocalization
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure.
Learning an Efficient Model of Hand Shape Variation From Depth Images
We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.
Depth-based hand pose estimation: methods, data, and challenges
To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
User-Specific Hand Modeling from Monocular Depth Sequences
We focus on modeling the human hand, and assume that a single rough template model is available.
Multi-Output Learning for Camera Relocalization
We formulate this problem as inversion of the generative rendering procedure, i. e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input.
Filter Forests for Learning Data-Dependent Convolutional Kernels
We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.
Decision Jungles: Compact and Rich Models for Classification
Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.
GeoF: Geodesic Forests for Learning Coupled Predictors
This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on.
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image.
