Search Results for author:
Found 15 papers, 7 papers with code
DUSt3R: Geometric 3D Vision Made Easy
Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera.
Cross-view and Cross-pose Completion for 3D Human Understanding
Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained on large datasets.
Win-Win: Training High-Resolution Vision Transformers from Two Windows
It is 4 times faster to train than a full-resolution network, and it is straightforward to use at test time compared to existing approaches.
SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction
Recent hand-object interaction datasets show limited real object variability and rely on fitting the MANO parametric model to obtain groundtruth hand shapes.
4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
This work presents 4DHumanOutfit, a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions.
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow.
Ranked #1 on
Optical Flow Estimation
on KITTI 2012
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
More precisely, we propose the pretext task of cross-view completion where the first input image is partially masked, and this masked content has to be reconstructed from the visible content and the second image.
MonoNHR: Monocular Neural Human Renderer
Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas.
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors
In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision.
SMPLy Benchmarking 3D Human Pose Estimation in the Wild
Predicting 3D human pose from images has seen great recent improvements.
DOPE: Distillation Of Part Experts for whole-body 3D pose estimation in the wild
We introduce DOPE, the first method to detect and estimate whole-body 3D human poses, including bodies, hands and faces, in the wild.
Robust Image Retrieval-based Visual Localization using Kapture
To demonstrate this, we present a versatile pipeline for visual localization that facilitates the use of different local and global features, 3D data (e. g. depth maps), non-vision sensor data (e. g. IMU, GPS, WiFi), and various processing algorithms.
Shape Reconstruction Using Volume Sweeping and Learned Photoconsistency
Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods.
Multi-View Dynamic Shape Refinement Using Local Temporal Integration
We consider 4D shape reconstructions in multi-view environments and investigate how to exploit temporal redundancy for precision refinement.
Ternary Neural Networks for Resource-Efficient AI Applications
In this paper, we propose ternary neural networks (TNNs) in order to make deep learning more resource-efficient.
