Search Results for author:
Found 17 papers, 6 papers with code
Lumiere: A Space-Time Diffusion Model for Video Generation
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis.
Ranked #6 on
Text-to-Video Generation
on UCF-101
Boundary Attention: Learning to Localize Boundaries under High Noise
We present a differentiable model that infers explicit boundaries, including curves, corners and junctions, using a mechanism that we call boundary attention.
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets.
WonderJourney: Going from Anywhere to Everywhere
We introduce WonderJourney, a modularized framework for perpetual 3D scene generation.
Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing.
Ranked #1 on
Semantic correspondence
on PF-PASCAL
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity.
A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
Text-to-image diffusion models have made significant advances in generating and editing high-quality images.
Ranked #3 on
Semantic correspondence
on SPair-71k
Self-supervised AutoFlow
Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric.
RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer
Self-supervised methods demonstrate learning scene flow estimation from unlabeled data, yet their accuracy lags behind (semi-)supervised methods.
Self-Supervised Multi-Frame Monocular Scene Flow
Estimating 3D scene flow from a sequence of monocular images has been gaining increased attention due to the simple, economical capture setup.
Self-Supervised Monocular Scene Flow Estimation
Our model achieves state-of-the-art accuracy among unsupervised/self-supervised learning approaches to monocular scene flow, and yields competitive results for the optical flow and monocular depth estimation sub-tasks.
Optical Flow Estimation in the Deep Learning Age
Akin to many subareas of computer vision, the recent advances in deep learning have also significantly influenced the literature on optical flow.
Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation
While leading to more accurate results, the downside of this is an increased number of parameters.
Ranked #10 on
Optical Flow Estimation
on KITTI 2012
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
By optionally fine-tuning on the KITTI training data, our method achieves competitive optical flow accuracy on the KITTI 2012 and 2015 benchmarks, thus in addition enabling generic pre-training of supervised networks for datasets with limited amounts of ground truth.
MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation
The key feature of our model is to fully exploit the symmetry properties that characterize optical flow and occlusions in the two consecutive images.
Joint Optical Flow and Temporally Consistent Semantic Segmentation
The importance and demands of visual scene understanding have been steadily increasing along with the active development of autonomous systems.
Generalized Deformable Spatial Pyramid: Geometry-Preserving Dense Correspondence Estimation
We present a Generalized Deformable Spatial Pyramid (GDSP) matching algorithm for calculating the dense correspondence between a pair of images with large appearance variations.
