The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify it semantically.
We introduce Diffusion Parametric Head Models (DPHMs), a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences.
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models.
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 manipulation methods can be misused to affect an individual's privacy or to spread disinformation.
1 code implementation • 10 Nov 2021 • Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhoefer, Vladislav Golyanik
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
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.
In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene.
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.
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.
Ranked #24 on Semantic Segmentation on Stanford2D3D Panoramic
Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality.
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction.
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.
Ranked #3 on 3D Object Recognition on ModelNet40
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure.