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Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Kangfu Mei , Vishal M. Patel

Given two consecutive RGB-D images, we propose a model that estimates a dense 3D motion field, also known as scene flow. We take advantage of the fact that in robot manipulation scenarios, scenes often consist of a set of rigidly moving…

Robotics · Computer Science 2018-07-25 Lin Shao , Parth Shah , Vikranth Dwaracherla , Jeannette Bohg

Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle visual tasks in challenging scenarios. However, due to…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Sheng Zhong , Zhongyang Ren , Xiya Zhu , Dehao Yuan , Cornelia Fermuller , Yi Zhou

Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many…

Machine Learning · Computer Science 2020-11-03 Xavier Alameda-Pineda , Vincent Drouard , Radu Horaud

In recent years modelling crowd and evacuation dynamics has become very important, with increasing huge numbers of people gathering around the world for many reasons and events. The fact that our global population grows dramatically every…

Physics and Society · Physics 2015-01-28 Mohamed H. Dridi

We describe a technique of image analysis providing the time resolved shape of a water surface simultaneously with the three-component velocity field on this surface. The method relies on a combination of stereoscopic surface mapping and…

Fluid Dynamics · Physics 2019-02-18 Quentin Aubourg , Joel Sommeria , Samuel Viboud , Nicolas Mordant

Video denoising is to remove noise from noise-corrupted data, thus recovering true signals via spatiotemporal processing. Existing approaches for spatiotemporal video denoising tend to suffer from motion blur artifacts, that is, the…

Computer Vision and Pattern Recognition · Computer Science 2020-01-23 Ce Wang , S. Kevin Zhou , Zhiwei Cheng

In this work, we aimed to replicate and extend the results presented in the DiffFluid paper[1]. The DiffFluid model showed that diffusion models combined with Transformers are capable of predicting fluid dynamics. It uses a denoising…

Fluid Dynamics · Physics 2025-07-14 Yannick Gachnang , Vismay Churiwala

We study the task of predicting dynamic physical properties from videos. More specifically, we consider physical properties that require temporal information to be inferred: elasticity of a bouncing object, viscosity of a flowing liquid,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Guanqi Zhan , Xianzheng Ma , Weidi Xie , Andrew Zisserman

Particle methods play an important role in computational fluid dynamics, but they are among the most difficult to implement and solve. The most common method is smoothed particle hydrodynamics, which is suitable for problem settings that…

Fluid Dynamics · Physics 2025-08-26 Masato Shibukawa , Naoya Ozaki , Maximilien Berthet

This paper presents WaveShot, an innovative portable unmanned surface vessel that aims to transform water surface videography by offering a highly maneuverable, cost-effective, and safe alternative to traditional filming methods. WaveShot…

Robotics · Computer Science 2024-08-14 Shijian Ma , Shicong Ma , Jianhao Jiao

Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of…

Computer Vision and Pattern Recognition · Computer Science 2018-03-07 Mohammad Babaeizadeh , Chelsea Finn , Dumitru Erhan , Roy H. Campbell , Sergey Levine

We propose a learning-based method that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera with known intrinsic calibration, our network…

Computer Vision and Pattern Recognition · Computer Science 2019-09-13 Kaixuan Wang , Shaojie Shen

Temporally consistent dense video annotations are scarce and hard to collect. In contrast, image segmentation datasets (and pre-trained models) are ubiquitous, and easier to label for any novel task. In this paper, we introduce a method to…

Computer Vision and Pattern Recognition · Computer Science 2022-03-18 Aharon Azulay , Tavi Halperin , Orestis Vantzos , Nadav Borenstein , Ofir Bibi

The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment…

Computer Vision and Pattern Recognition · Computer Science 2021-04-06 Sebastian Bullinger , Christoph Bodensteiner , Michael Arens

We consider here probabilistic models of transportation flows. The main goal of this introduction is rather not to present various techniques for problem solving but to present some intuition to invent adequate and natural models having…

Probability · Mathematics 2011-10-24 V. A. Malyshev , A. A. Zamyatin

To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating…

Computer Vision and Pattern Recognition · Computer Science 2015-12-08 Mark Sheinin , Yoav Y. Schechner

In the present manuscript, we consider the practical problem of wave interaction with a vertical wall. However, the novelty here consists in the fact that the wall can move horizontally due to a system of springs. The water wave evolution…

Fluid Dynamics · Physics 2020-02-20 Gayaz Khakimzyanov , Denys Dutykh

Normalizing flow is a generative modeling approach with efficient sampling. However, Flow-based models suffer two issues: 1) If the target distribution is manifold, due to the unmatch between the dimensions of the latent target distribution…

Machine Learning · Computer Science 2024-04-24 Qinglong Meng , Chongkun Xia , Xueqian Wang

Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on neural radiance field or 3D Gaussian splatting are limited…

Computer Vision and Pattern Recognition · Computer Science 2025-07-04 Jiahao Wu , Rui Peng , Jianbo Jiao , Jiayu Yang , Luyang Tang , Kaiqiang Xiong , Jie Liang , Jinbo Yan , Runling Liu , Ronggang Wang
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