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Found 11 papers, 7 papers with code
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
We demonstrate the versatility of our representation on a variety of articulated objects from common datasets and obtain reposable 3D reconstructions without the need of object-specific skeletal templates.
Generative Neural Articulated Radiance Fields
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress.
Fast Training of Neural Lumigraph Representations using Meta Learning
Inspired by neural variants of image-based rendering, we develop a new neural rendering approach with the goal of quickly learning a high-quality representation which can also be rendered in real-time.
Neural Lumigraph Rendering
Novel view synthesis is a challenging and ill-posed inverse rendering problem.
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering.
Ranked #3 on
Scene Generation
on VizDoom
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Finally, we demonstrate an application of our model for estimating customer attention in a supermarket setting.
Ranked #4 on
Gaze Estimation
on Gaze360
Learning the signatures of the human grasp using a scalable tactile glove
Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135, 000 frames, each covering the full hand, while interacting with 26 different objects.
Neural Inverse Knitting: From Images to Manufacturing Instructions
Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting.
Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task.
A Dataset of Flash and Ambient Illumination Pairs from the Crowd
We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash photography and other applications that can benefit from having separate illuminations.
Eye Tracking for Everyone
We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.
