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Found 16 papers, 8 papers with code
GAF: Gaussian Avatar Reconstruction from Monocular Videos via Multi-view Diffusion
We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones.
HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
First, we register a parametric head model with vertex displacements to each mesh of the recently introduced NPHM dataset of accurate 3D head scans.
DPHMs: Diffusion Parametric Head Models for Depth-based Tracking
We introduce Diffusion Parametric Head Models (DPHMs), a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences.
Generating Context-Aware Natural Answers for Questions in 3D Scenes
3D question answering is a young field in 3D vision-language that is yet to be explored.
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models.
On the Exploitation of Deepfake Model Recognition
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.
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations
Face manipulation methods can be misused to affect an individual's privacy or to spread disinformation.
Advances in Neural Rendering
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
CAD-Deform: Deformable Fitting of CAD Models to 3D Scans
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
In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene.
NRMVS: Non-Rigid Multi-View Stereo
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.
Spherical CNNs on Unstructured Grids
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
Parsing Geometry Using Structure-Aware Shape Templates
Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality.
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction.
Volumetric and Multi-View CNNs for Object Classification on 3D Data
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
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.
