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Found 20 papers, 9 papers with code
Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation
We introduce MOTIA Mastering Video Outpainting Through Input-Specific Adaptation, a diffusion-based pipeline that leverages both the intrinsic data-specific patterns of the source video and the image/video generative prior for effective outpainting.
AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning
We validate the proposed strategy in image-conditioned video generation and layout-conditioned video generation, all achieving top-performing results.
Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modeling
For the first stage, we propose a diffusion-based motion field predictor, which focuses on deducing the trajectories of the reference image's pixels.
InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation
In this work, we introduce InstructSeq, an instruction-conditioned multi-modal modeling framework that unifies diverse vision tasks through flexible natural language control and handling of both visual and textual data.
FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow
This paper introduces a novel transformer-based network architecture, FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for pretraining it to tackle the problem of optical flow estimation.
Context-PIPs: Persistent Independent Particles Demands Spatial Context Features
We tackle the problem of Persistent Independent Particles (PIPs), also called Tracking Any Point (TAP), in videos, which specifically aims at estimating persistent long-term trajectories of query points in videos.
DiffInDScene: Diffusion-based High-Quality 3D Indoor Scene Generation
In addition to the scene generation, the final part of DiffInDScene can be used as a post-processing module to refine the 3D reconstruction results from multi-view stereo.
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation
We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner.
PATS: Patch Area Transportation with Subdivision for Local Feature Matching
However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs.
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation
BlinkSim consists of a configurable rendering engine and a flexible engine for event data simulation.
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
FlowFormer introduces a transformer architecture into optical flow estimation and achieves state-of-the-art performance.
CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e. g., controlling the shapes, expressions, textures, and poses of the generated face images.
NeuralMarker: A Framework for Learning General Marker Correspondence
We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker.
Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields
Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e. g., controlling the shapes, expressions, textures, and poses of the generated face images.
FlowFormer: A Transformer Architecture for Optical Flow
We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.
Ranked #1 on
Optical Flow Estimation
on Sintel-final
VS-Net: Voting with Segmentation for Visual Localization
To address this problem, we propose a novel visual localization framework that establishes 2D-to-3D correspondences between the query image and the 3D map with a series of learnable scene-specific landmarks.
LIFE: Lighting Invariant Flow Estimation
However, local image contents are inevitably ambiguous and error-prone during the cross-image feature matching process, which hinders downstream tasks.
R-SAC: Reinforcement Sample Consensus
In contrast, a model estimated from more observations may be better than from a minimum set.
SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks
To suit our network to self-supervised learning, we design several novel loss functions that utilize the inherent properties of LiDAR point clouds.
Prior Guided Dropout for Robust Visual Localization in Dynamic Environments
Additionally, the dropout module enables the pose regressor to output multiple hypotheses from which the uncertainty of pose estimates can be quantified and leveraged in the following uncertainty-aware pose-graph optimization to improve the robustness further.
