TrajectoryMover: Generative Movement of Object Trajectories in Videos
arXiv:2603.29092v1 Announce Type: new Abstract: Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve, especially for non-expert editors. Existing methods focus on prescribing an object's 3D or 2D motion trajectory in a video, or on altering the appearance of an object or a scene, while preserving both the video's plausibility and identity. Yet a method to move an object's 3D motion trajectory in a video, i.e., moving an object while preserving its relative 3D motion, is currently still missing. The main challenge lies in obtaining paired video data for this scenario. Previous methods typically rely on clever data generation approaches to construct plausible paired data from unpaired videos, but
View PDF HTML (experimental)
Abstract:Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve, especially for non-expert editors. Existing methods focus on prescribing an object's 3D or 2D motion trajectory in a video, or on altering the appearance of an object or a scene, while preserving both the video's plausibility and identity. Yet a method to move an object's 3D motion trajectory in a video, i.e., moving an object while preserving its relative 3D motion, is currently still missing. The main challenge lies in obtaining paired video data for this scenario. Previous methods typically rely on clever data generation approaches to construct plausible paired data from unpaired videos, but this approach fails if one of the videos in a pair can not easily be constructed from the other. Instead, we introduce TrajectoryAtlas, a new data generation pipeline for large-scale synthetic paired video data and a video generator TrajectoryMover fine-tuned with this data. We show that this successfully enables generative movement of object trajectories. Project page: this https URL
Comments: 24 pages, 8 figures. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2603.29092 [cs.CV]
(or arXiv:2603.29092v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2603.29092
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kiran Chhatre [view email] [v1] Tue, 31 Mar 2026 00:15:36 UTC (8,432 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
announcearxivgithub
CellSecInspector: Safeguarding Cellular Networks via Automated Security Analysis on Specifications
arXiv:2512.24682v3 Announce Type: replace Abstract: The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requirements, fail to capture deep semantic dependencies, cross-sentence/clause relationships, and evolving specification behaviors. In this work, we present CellSecInspector, an automated framework for security analysis of 3GPP specifications. CellSecInspector extracts structured state-condition-action (SCA) representations, models mobile network procedures with comprehensive function chains, systematically validates them against 9 foundational se
Deep Recurrent Hidden Markov Learning Framework for Multi-Stage Advanced Persistent Threat Prediction
arXiv:2601.06734v2 Announce Type: replace Abstract: Advanced Persistent Threats (APTs) represent hidden, multi\-stage cyberattacks whose long term persistence and adaptive behavior challenge conventional intrusion detection systems (IDS). Although recent advances in machine learning and probabilistic modeling have improved APT detection performance, most existing approaches remain reactive and alert\-centric, providing limited capability for stage-aware prediction and principled inference under uncertainty, particularly when observations are sparse or incomplete. This paper proposes E\-HiDNet, a unified hybrid deep probabilistic learning framework that integrates convolutional and recurrent neural networks with a Hidden Markov Model (HMM) to allow accurate prediction of the progression of
Beyond Metadata: Multimodal, Policy-Aware Detection of YouTube Scam Videos
arXiv:2509.23418v2 Announce Type: replace Abstract: YouTube is a major platform for information and entertainment, but its wide accessibility also makes it attractive for scammers to upload deceptive or malicious content. Prior detection approaches rely largely on textual or statistical metadata, such as titles, descriptions, view counts, or likes, which are effective in many cases but can be evaded through benign-looking text, manipulated statistics, or other obfuscation strategies (e.g., 'Leetspeak'), while ignoring visual cues. In this study, we systematically investigate multimodal approaches for detecting YouTube scams. Our dataset consolidates established scam categories and augments them with full-length videos and policy-grounded reasoning annotations. Experiments show that a text-
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
A Hitchhiker's Guide to Privacy-Preserving Digital Payment Systems: A Survey on Anonymity, Confidentiality, and Auditability
arXiv:2505.21008v3 Announce Type: replace Abstract: Crypto-assets and central bank digital currencies (CBDCs) are reshaping how value is exchanged in distributed computing environments. These systems combine cryptographic primitives, protocol design, and system architectures to provide transparency and efficiency while raising critical challenges around privacy and regulatory compliance. This survey offers a comprehensive overview of privacy-preserving digital payment systems, covering both decentralized ledger systems and CBDCs. We present a taxonomy of privacy goals -- including anonymity, confidentiality, unlinkability, and auditability -- and map them to the cryptographic primitives, protocols, and system architectures that implement them. Our work adopts a design-oriented perspective,
Rambus Unveils HBM4E Controller: 16 GT/s, 2,048-Bit Interface, Enabling C-HBM4E
Rambus launches 16 GT/s HBM4E controller for AI and HPC, pushing 4 TB/s bandwidth per stack. The post Rambus Unveils HBM4E Controller: 16 GT/s, 2,048-Bit Interface, Enabling C-HBM4E appeared first on EE Times . ]]>
Transcript for Infinity, Paradoxes, Gödel Incompleteness the Mathematical Multiverse Lex Fridman Podcast #488
This is a transcript of Lex Fridman Podcast #488 with Joel David Hamkins. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Go back to this episode s main page Watch the full YouTube version of the podcast Table of Contents Here are the loose chapters in the conversation. Click link to jump approximately to that part in the transcript: 0:00 Introduction 2:17 Infinity paradoxes 49:27 Russell s paradox 1:02:35
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!