Search Results for author:
Found 18 papers, 7 papers with code
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime.
Ranked #1 on
Feature Upsampling
on ImageNet
DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos
Second, we analyze the sensitivity of trackers to visual artifacts in real scenes and motivate the idea of running assistive keypoint selectors alongside trackers.
Fast View Synthesis of Casual Videos
Specifically, we build a global static scene model using an extended plane-based scene representation to synthesize temporally coherent novel video.
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Unsupervised semantic segmentation aims to discover and localize semantically meaningful categories within image corpora without any form of annotation.
Ranked #4 on
Unsupervised Semantic Segmentation
on Potsdam-3
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.
Consistent Depth of Moving Objects in Video
We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera.
Editing Conditional Radiance Fields
In this paper, we explore enabling user editing of a category-level NeRF - also known as a conditional radiance field - trained on a shape category.
Ranked #1 on
Novel View Synthesis
on PhotoShape
End-to-End Optimization of Scene Layout
Experiments suggest that our model achieves higher accuracy and diversity in conditional scene synthesis and allows exemplar-based scene generation from various input forms.
Deep Audio Priors Emerge From Harmonic Convolutional Networks
We show that networks using Harmonic Convolution can reliably model audio priors and achieve high performance in unsupervised audio restoration tasks.
Learning to Reconstruct Shapes from Unseen Classes
From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life.
Visual Object Networks: Image Generation with Disentangled 3D Representation
Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes.
Visual Object Networks: Image Generation with Disentangled 3D Representations
The VON not only generates images that are more realistic than the state-of-the-art 2D image synthesis methods but also enables many 3D operations such as changing the viewpoint of a generated image, shape and texture editing, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.
Learning Shape Priors for Single-View 3D Completion and Reconstruction
The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects.
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only.
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
We study 3D shape modeling from a single image and make contributions to it in three aspects.
Ranked #1 on
3D Shape Classification
on Pix3D
Shape and Material from Sound
Hearing an object falling onto the ground, humans can recover rich information including its rough shape, material, and falling height.
Generative Modeling of Audible Shapes for Object Perception
Humans infer rich knowledge of objects from both auditory and visual cues.
Light Field From Micro-Baseline Image Pair
We first introduce a disparity assisted phase based synthesis (DAPS) strategy that can integrate disparity infor- mation into the phase term of a reference image to warp it to its close neighbor views.
