Search Results for author:
Found 17 papers, 9 papers with code
Tracking Everything Everywhere All at Once
We present a new test-time optimization method for estimating dense and long-range motion from a video sequence.
Persistent Nature: A Generative Model of Unbounded 3D Worlds
Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions.
Ranked #2 on
Scene Generation
on GoogleEarth
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video
Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object.
DynIBaR: Neural Dynamic Image-Based Rendering
Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects, but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories.
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene.
Neural 3D Reconstruction in the Wild
We are witnessing an explosion of neural implicit representations in computer vision and graphics.
3D Moments from Near-Duplicate Photos
As output, we produce a video that smoothly interpolates the scene motion from the first photo to the second, while also producing camera motion with parallax that gives a heightened sense of 3D.
Deformable Sprites for Unsupervised Video Decomposition
We describe a method to extract persistent elements of a dynamic scene from an input video.
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images
We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines.
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes
We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input.
Crowdsampling the Plenoptic Function
These photos represent a sparse and unstructured sampling of the plenoptic function for a particular scene.
UprightNet: Geometry-Aware Camera Orientation Estimation from Single Images
We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene.
Learning the Depths of Moving People by Watching Frozen People
We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving.
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
Intrinsic image decomposition is a challenging, long-standing computer vision problem for which ground truth data is very difficult to acquire.
Learning Intrinsic Image Decomposition from Watching the World
However, it is difficult to collect ground truth training data at scale for intrinsic images.
MegaDepth: Learning Single-View Depth Prediction from Internet Photos
We validate the use of large amounts of Internet data by showing that models trained on MegaDepth exhibit strong generalization-not only to novel scenes, but also to other diverse datasets including Make3D, KITTI, and DIW, even when no images from those datasets are seen during training.
