Search Results for author:
Found 14 papers, 5 papers with code
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.
Ranked #1 on
Stereo Depth Estimation
on KITTI2015
Nerfies: Deformable Neural Radiance Fields
We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones.
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space.
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
Towards Real-time Simulation of Hyperelastic Materials
In this paper, we show that Projective Dynamics can be interpreted as a quasi-Newton method.
Graphics
CvxNet: Learnable Convex Decomposition
We introduce a network architecture to represent a low dimensional family of convexes.
Dynamic 3D Avatar Creation from Hand-held Video Input
From this user- and expression-specific data, we learn a regressor for on-the-fly detail synthesis during animation to enhance the perceptual realism of the avatars.
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts.
Deep Implicit Volume Compression
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.
GeLaTO: Generative Latent Textured Objects
Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem.
Improved Detection of Face Presentation Attacks Using Image Decomposition
Presentation attack detection (PAD) is a critical component in secure face authentication.
Multiresolution Deep Implicit Functions for 3D Shape Representation
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields
Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized by the scene's plenoptic function.
