Search Results for author:
Found 14 papers, 6 papers with code
TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization
In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning.
Ranked #1 on
Image Manipulation Detection
on Columbia
NewsStories: Illustrating articles with visual summaries
Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images.
Learning 3D Semantic Segmentation with only 2D Image Supervision
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.
Ranked #8 on
LIDAR Semantic Segmentation
on nuScenes
Differentiable Surface Rendering via Non-Differentiable Sampling
We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement.
MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date.
Ranked #1 on
3D Human Pose Estimation
on SkiPose
Optical Mouse: 3D Mouse Pose From Single-View Video
We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos.
Human 3D keypoints via spatial uncertainty modeling
We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint.
Learning to Infer Semantic Parameters for 3D Shape Editing
Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e. g., the pose of a person's arm or the length of an airplane's wing) while preserving as much existing details as possible.
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
We show that, under certain assumptions, this combination yields a deep neural likelihood-based minimization objective that attains a known lower bound upon convergence.
Local Implicit Grid Representations for 3D Scenes
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
Local Deep Implicit Functions for 3D Shape
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
Latent feature disentanglement for 3D meshes
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR.
Cross-Domain 3D Equivariant Image Embeddings
This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
Eyemotion: Classifying facial expressions in VR using eye-tracking cameras
One of the main challenges of social interaction in virtual reality settings is that head-mounted displays occlude a large portion of the face, blocking facial expressions and thereby restricting social engagement cues among users.
