Search Results for author: Matthias Niessner

Found 15 papers, 8 papers with code

HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs

no code implementations21 Dec 2023 Artem Sevastopolsky, Philip-William Grassal, Simon Giebenhain, ShahRukh Athar, Luisa Verdoliva, Matthias Niessner

The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify it semantically.

DPHMs: Diffusion Parametric Head Models for Depth-based Tracking

no code implementations2 Dec 2023 Jiapeng Tang, Angela Dai, Yinyu Nie, Lev Markhasin, Justus Thies, Matthias Niessner

We introduce Diffusion Parametric Head Models (DPHMs), a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences.

On the Exploitation of Deepfake Model Recognition

no code implementations9 Apr 2022 Luca Guarnera, Oliver Giudice, Matthias Niessner, Sebastiano Battiato

Despite recent advances in Generative Adversarial Networks (GANs), with special focus to the Deepfake phenomenon there is no a clear understanding neither in terms of explainability nor of recognition of the involved models.

Face Swapping

CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

1 code implementation ECCV 2020 Vladislav Ishimtsev, Alexey Bokhovkin, Alexey Artemov, Savva Ignatyev, Matthias Niessner, Denis Zorin, Evgeny Burnaev

Shape retrieval and alignment are a promising avenue towards turning 3D scans into lightweight CAD representations that can be used for content creation such as mobile or AR/VR gaming scenarios.


Active Scene Understanding via Online Semantic Reconstruction

no code implementations18 Jun 2019 Lintao Zheng, Chenyang Zhu, Jiazhao Zhang, Hang Zhao, Hui Huang, Matthias Niessner, Kai Xu

In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene.

Scene Understanding Semantic Segmentation

NRMVS: Non-Rigid Multi-View Stereo

no code implementations12 Jan 2019 Matthias Innmann, Kihwan Kim, Jinwei Gu, Matthias Niessner, Charles Loop, Marc Stamminger, Jan Kautz

We show that creating a dense 4D structure from a few RGB images with non-rigid changes is possible, and demonstrate that our method can be used to interpolate novel deformed scenes from various combinations of these deformation estimates derived from the sparse views.

3D Reconstruction Depth Estimation

Spherical CNNs on Unstructured Grids

1 code implementation ICLR 2019 Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner

We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.

Semantic Segmentation

Parsing Geometry Using Structure-Aware Shape Templates

1 code implementation 3D Vision 2018 2018 Vignesh Ganapathi-Subramanian, Olga Diamanti, Soeren Pirk, Chengcheng Tang, Matthias Niessner, Leonidas J. Guibas

Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality.

Object Object Recognition +1

Volumetric and Multi-View CNNs for Object Classification on 3D Data

2 code implementations CVPR 2016 Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, Leonidas J. Guibas

Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.

3D Object Recognition 3D Point Cloud Classification +1

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