Search Results for author:
Found 27 papers, 15 papers with code
L4P: Low-Level 4D Vision Perception Unified
Moreover, it solves all those tasks at once in a time comparable to that of individual single-task methods.
Zero-Shot Monocular Scene Flow Estimation in the Wild
Large models have shown generalization across datasets for many low-level vision tasks, like depth estimation, but no such general models exist for scene flow.
FoundationStereo: Zero-Shot Stereo Matching
However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching.
nvTorchCam: An Open-source Library for Camera-Agnostic Differentiable Geometric Vision
We introduce nvTorchCam, an open-source library under the Apache 2. 0 license, designed to make deep learning algorithms camera model-independent.
FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization
Unfortunately, most of the GT data is for pinhole cameras, making it impossible to properly train depth estimation models for large-FoV cameras.
Zero-shot Pose Transfer for Unrigged Stylized 3D Characters
We present a zero-shot approach that requires only the widely available deformed non-stylized avatars in training, and deforms stylized characters of significantly different shapes at inference.
Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos
To tackle it, we first introduce a large-scale dataset of real and synthetic videos of people interacting on a video call, where the synthetic videos are generated using the facial appearance of one person and the expressions of another.
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects
Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies.
Efficient Geometry-aware 3D Generative Adversarial Networks
Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge.
Neural Trajectory Fields for Dynamic Novel View Synthesis
Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes.
Noise-Aware Video Saliency Prediction
We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observers varies with the frame, as it depends on the content of the scene.
Binary TTC: A Temporal Geofence for Autonomous Navigation
Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene -- even for humans.
Generative View Synthesis: From Single-view Semantics to Novel-view Images
We propose to push the envelope further, and introduce Generative View Synthesis (GVS), which can synthesize multiple photorealistic views of a scene given a single semantic map.
Bi3D: Stereo Depth Estimation via Binary Classifications
Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels.
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera
Our insight is that although its scale and quality are inconsistent with other views, the depth estimation from a single view can be used to reason about the globally coherent geometry of dynamic contents.
Meshlet Priors for 3D Mesh Reconstruction
Meshlets act as a dictionary of local features and thus allow to use learned priors to reconstruct object meshes in any pose and from unseen classes, even when the noise is large and the samples sparse.
Video Stitching for Linear Camera Arrays
Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts.
Pixel-Adaptive Convolutional Neural Networks
In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.
Extreme View Synthesis
We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small--as few as two.
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras.
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference.
Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms.
Deep Learning with Energy-efficient Binary Gradient Cameras
Power consumption is a critical factor for the deployment of embedded computer vision systems.
Loss Functions for Neural Networks for Image Processing
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems.
Locally Non-rigid Registration for Mobile HDR Photography
Image registration for stack-based HDR photography is challenging.
HDR Deghosting: How to Deal with Saturation?
We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes.
