136 citations · 305 across the 24 of their papers we have counts for
25 papers
ORBIT++: Benchmarking SfM in the Wild with 360° Video
Sara Sabour, Linyi Jin, Richard Tucker +9
Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging camera motions or dynamic scenes.…
Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis
Michal Geyer, Omer Tov, Linyi Jin +4
The rising popularity of immersive visual experiences has increased interest in stereoscopic 3D video generation. Despite significant advances in video synthesis, creating 3D video…
MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videos
Zhengqi Li, Richard Tucker, Forrester Cole +6
We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional stru…
Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos
Linyi Jin, Richard Tucker, Zhengqi Li +3
Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction. Yet, unlike other problems where large-scale superv…
Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusion
Boyang Deng, Richard Tucker, Zhengqi Li +3
We present a method for generating Streetscapes-long sequences of views through an on-the-fly synthesized city-scale scene. Our generation is conditioned by language input (e.g., c…
Generative Image Dynamics
Zhengqi Li, Richard Tucker, Noah Snavely +1
We present an approach to modeling an image-space prior on scene motion. Our prior is learned from a collection of motion trajectories extracted from real video sequences depicting…
Persistent Nature: A Generative Model of Unbounded 3D Worlds
Lucy Chai, Richard Tucker, Zhengqi Li +2
Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of…
DynIBaR: Neural Dynamic Image-Based Rendering
Zhengqi Li, Qianqian Wang, Forrester Cole +2
We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene. State-of-the-art methods based on temporally varying Neural Radiance Fi…
Deformable Sprites for Unsupervised Video Decomposition
Vickie Ye, Zhengqi Li, Richard Tucker +2
We describe a method to extract persistent elements of a dynamic scene from an input video. We represent each scene element as a \emph{Deformable Sprite} consisting of three compon…
Simple and Effective Synthesis of Indoor 3D Scenes
Jing Yu Koh, Harsh Agrawal, Dhruv Batra +6
We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including v…
Dimensions of Motion: Monocular Prediction through Flow Subspaces
Richard Strong Bowen, Richard Tucker, Ramin Zabih +1
We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompa…
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting
Varun Jampani, Huiwen Chang, Kyle Sargent +8
Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve comp…
Consistent Depth of Moving Objects in Video
Zhoutong Zhang, Forrester Cole, Richard Tucker +2
We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera. We seek a geometrically and tem…
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control
Tomas Jakab, Richard Tucker, Ameesh Makadia +3
We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D obje…
De-rendering the World's Revolutionary Artefacts
Shangzhe Wu, Ameesh Makadia, Jiajun Wu +3
Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explic…
Repopulating Street Scenes
Yifan Wang, Andrew Liu, Richard Tucker +4
We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Appli…
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
Andrew Liu, Richard Tucker, Varun Jampani +3
We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a…
MetaSDF: Meta-learning Signed Distance Functions
Vincent Sitzmann, Eric R. Chan, Richard Tucker +2
Neural implicit shape representations are an emerging paradigm that offers many potential benefits over conventional discrete representations, including memory efficiency at a high…
Single-View View Synthesis with Multiplane Images
Richard Tucker, Noah Snavely
A recent strand of work in view synthesis uses deep learning to generate multiplane images (a camera-centric, layered 3D representation) given two or more input images at known vie…
Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
Pratul P. Srinivasan, Ben Mildenhall, Matthew Tancik +3
We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches…
DeepView: View Synthesis with Learned Gradient Descent
John Flynn, Michael Broxton, Paul Debevec +5
We present a novel approach to view synthesis using multiplane images (MPIs). Building on recent advances in learned gradient descent, our algorithm generates an MPI from a set of…
Pushing the Boundaries of View Extrapolation with Multiplane Images
Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron +3
We explore the problem of view synthesis from a narrow baseline pair of images, and focus on generating high-quality view extrapolations with plausible disocclusions. Our method bu…
Learning the Depths of Moving People by Watching Frozen People
Zhengqi Li, Tali Dekel, Forrester Cole +4
We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving. Existing methods for recovering depth for dynam…
Layer-structured 3D Scene Inference via View Synthesis
Shubham Tulsiani, Richard Tucker, Noah Snavely
We present an approach to infer a layer-structured 3D representation of a scene from a single input image. This allows us to infer not only the depth of the visible pixels, but als…