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Found 18 papers, 12 papers with code
Scaling 4D Representations
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video.
TAPVid-3D: A Benchmark for Tracking Any Point in 3D
We introduce a new benchmark, TAPVid-3D, for evaluating the task of long-range Tracking Any Point in 3D (TAP-3D).
BootsTAP: Bootstrapped Training for Tracking-Any-Point
To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes.
Ranked #1 on
Point Tracking
on TAP-Vid-RGB-Stacking
Replay: Multi-modal Multi-view Acted Videos for Casual Holography
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially.
CoTracker: It is Better to Track Together
We introduce CoTracker, a transformer-based model that tracks a large number of 2D points in long video sequences.
Ranked #2 on
Point Tracking
on TAP-Vid-Kinetics-First
DynamicStereo: Consistent Dynamic Depth from Stereo Videos
The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions.
Real-time volumetric rendering of dynamic humans
We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits.
Self-Supervised Correspondence Estimation via Multiview Registration
To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences.
Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors.
BodyMap: Learning Full-Body Dense Correspondence Map
Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction.
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos
Compared to weaker deformation models, this significantly reduces the reconstruction ambiguity and, for dynamic objects, allows Keypoint Transporter to obtain reconstructions of the quality superior or at least comparable to prior approaches while being much faster and reliant on a pre-trained monocular depth estimator network.
Reconstructing and grounding narrated instructional videos in 3D
Contrary to the standard scenario of instance-level 3D reconstruction, where identical objects or scenes are present in all views, objects in different instructional videos may have large appearance variations given varying conditions and versions of the same product.
Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
In this work we target the problem of estimating accurately localised correspondences between a pair of images.
Is This The Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization
The pose with the largest geometric consistency with the query image, e. g., in the form of an inlier count, is then selected in a second stage.
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
Ranked #8 on
Image Matching
on IMC PhotoTourism
Neighbourhood Consensus Networks
Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.
Ranked #2 on
Semantic correspondence
on PF-PASCAL
(PCK (weak) metric)
End-to-end weakly-supervised semantic alignment
We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category.
Convolutional neural network architecture for geometric matching
We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters.
