Search Results for author:
Found 27 papers, 9 papers with code
Materialistic: Selecting Similar Materials in Images
We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area.
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene.
Unsupervised Discovery and Composition of Object Light Fields
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding.
Learning to generate line drawings that convey geometry and semantics
We introduce a geometry loss which predicts depth information from the image features of a line drawing, and a semantic loss which matches the CLIP features of a line drawing with its corresponding photograph.
What You Can Learn by Staring at a Blank Wall
We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room.
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
In this work, we propose a novel neural scene representation, Light Field Networks or LFNs, which represent both geometry and appearance of the underlying 3D scene in a 360-degree, four-dimensional light field parameterized via a neural implicit representation.
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations
We find that learning category and viewpoint in separate networks compared to a shared one leads to an increase in selectivity and invariance, as separate networks are not forced to preserve information about both category and viewpoint.
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region.
A Dataset of Multi-Illumination Images in the Wild
Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation.
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions
We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension.
Flexible SVBRDF Capture with a Multi-Image Deep Network
Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph.
Graphics I.3
Single-Image SVBRDF Capture with a Rendering-Aware Deep Network
Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures.
Graphics I.3
Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks
We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image.
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'.
Synthesizing Images of Humans in Unseen Poses
Given an image of a person and a desired pose, we produce a depiction of that person in that pose, retaining the appearance of both the person and background.
Turning Corners Into Cameras: Principles and Methods
We show that walls and other obstructions with edges can be exploited as naturally-occurring "cameras" that reveal the hidden scenes beyond them.
Understanding Infographics through Textual and Visual Tag Prediction
And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i. e. visual hashtags.
Learning Visual Importance for Graphic Designs and Data Visualizations
Our models are neural networks trained on human clicks and importance annotations on hundreds of designs.
Visual Vibrometry: Estimating Material Properties from Small Motions in Video
The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering.
Visual Vibrometry: Estimating MaterialProperties from Small Motions in Video
The estimation of material properties is important for scene understanding, with many applications in vision, robotics, andstructural engineering.
BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention
In this paper, we present BubbleView, an alternative methodology for eye tracking using discrete mouse clicks to measure which information people consciously choose to examine.
A Video-Based Method for Objectively Rating Ataxia
The performance of our system is comparable to that of a group of ataxia specialists in terms of mean error and correlation, and our system's predictions were consistently within the range of inter-rater variability.
Reflection Removal Using Ghosting Cues
For single-pane windows, ghosting cues arise from shifted reflections on the two surfaces of the glass pane.
Visual Vibrometry: Estimating Material Properties From Small Motion in Video
The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering.
The Aperture Problem for Refractive Motion
We pose and solve a generalization of the aperture problem for moving refractive elements.
Video Magnification in Presence of Large Motions
Video magnification reveals subtle variations that would be otherwise invisible to the naked eye.
Detecting Pulse from Head Motions in Video
We extract heart rate and beat lengths from videos by measuring subtle head motion caused by the Newtonian reaction to the influx of blood at each beat.
