Stefanie Wuhrer

Bio: Stefanie Wuhrer is an academic researcher from University of Grenoble. The author has contributed to research in topics: Control reconfiguration & Invariant (mathematics). The author has an hindex of 26, co-authored 109 publications receiving 2022 citations. Previous affiliations of Stefanie Wuhrer include Saarland University & Carleton University.

3D Morphable Face Models—Past, Present, and Future

Abstract: In this article, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely, capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research, and highlighting the broad range of current and future applications.

3D Morphable Face Models -- Past, Present and Future

Abstract: In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications.

Estimation of Human Body Shape in Motion with Wide Clothing

Abstract: Estimating 3D human body shape in motion from a sequence of unstructured oriented 3D point clouds is important for many applications. We propose the first automatic method to solve this problem that works in the presence of loose clothing. The problem is formulated as an optimization problem that solves for identity and posture parameters in a shape space capturing likely body shape variations. The automation is achieved by leveraging a recent robust pose detection method [1]. To account for clothing, we take advantage of motion cues by encouraging the estimated body shape to be inside the observations. The method is evaluated on a new benchmark containing different subjects, motions, and clothing styles that allows to quantitatively measure the accuracy of body shape estimates. Furthermore, we compare our results to existing methods that require manual input and demonstrate that results of similar visual quality can be obtained.

The Analysis of Time Series: An Introduction

Abstract: The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

Abstract: We introduce Pixel-aligned Implicit Function (PIFu), an implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu produces high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

FAUST: Dataset and Evaluation for 3D Mesh Registration

Abstract: New scanning technologies are increasing the importance of 3D mesh data and the need for algorithms that can reliably align it Surface registration is important for building full 3D models from partial scans, creating statistical shape models, shape retrieval, and tracking The problem is particularly challenging for non-rigid and articulated objects like human bodies While the challenges of real-world data registration are not present in existing synthetic datasets, establishing ground-truth correspondences for real 3D scans is difficult We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments We define a new dataset called FAUST that contains 300 scans of 10 people in a wide range of poses together with an evaluation methodology To achieve accurate registration, we paint the subjects with high-frequency textures and use an extensive validation process to ensure accurate ground truth We find that current shape registration methods have trouble with this real-world data The dataset and evaluation website are available for research purposes at http://faustistuempgde