Focus360: Guiding User Attention in Immersive Videos for VR
arXiv:2603.28774v1 Announce Type: new Abstract: This demo introduces Focus360, a system designed to enhance user engagement in 360{\deg} VR videos by guiding attention to key elements within the scene. Using natural language descriptions, the system identifies important elements and applies a combination of visual effects to guide attention seamlessly. At the demonstration venue, participants can experience a 360{\deg} Safari Tour, showcasing the system's ability to improve user focus while maintaining an immersive experience.
View PDF HTML (experimental)
Abstract:This demo introduces Focus360, a system designed to enhance user engagement in 360° VR videos by guiding attention to key elements within the scene. Using natural language descriptions, the system identifies important elements and applies a combination of visual effects to guide attention seamlessly. At the demonstration venue, participants can experience a 360° Safari Tour, showcasing the system's ability to improve user focus while maintaining an immersive experience.
Comments: 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
Subjects:
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
Cite as: arXiv:2603.28774 [cs.HC]
(or arXiv:2603.28774v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2603.28774
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1109/VRW66409.2025.00462
DOI(s) linking to related resources
Submission history
From: Paulo Vitor Santana Silva [view email] [v1] Thu, 29 Jan 2026 19:29:32 UTC (5,280 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
announcearxiv
Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design
Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, — Oihan Cordelier, Youssef Diouane, Nathalie Bartoli
Transfer Learning in Bayesian Optimization for Aircraft Design
The use of transfer learning within Bayesian optimization addresses the disadvantages of the so-called \textit{cold start} problem by using source data to aid in the optimization of a target problem. We present a method that leverages an ensemble of surrogate models using transfer learning and integrates it in a constrained Bayesian optimization framework. We identify challenges particular to aircraft design optimization related to heterogeneous design variables and constraints. We propose the use of a partial-least-squares dimension reduction algorithm to address design space heterogeneity, a — Ali Tfaily, Youssef Diouane, Nathalie Bartoli
Symmetrizing Bregman Divergence on the Cone of Positive Definite Matrices: Which Mean to Use and Why
This work uncovers variational principles behind symmetrizing the Bregman divergences induced by generic mirror maps over the cone of positive definite matrices. We show that computing the canonical means for this symmetrization can be posed as minimizing the desired symmetrized divergences over a set of mean functionals defined axiomatically to satisfy certain properties. For the forward symmetrization, we prove that the arithmetic mean over the primal space is canonical for any mirror map over the positive definite cone. For the reverse symmetrization, we show that the canonical mean is the — Tushar Sial, Abhishek Halder
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
TigerFS Mounts PostgreSQL Databases as a Filesystem for Developers and AI Agents
TigerFS is a new experimental filesystem that mounts a database as a directory and stores files directly in PostgreSQL. The open source project exposes database data through a standard filesystem interface, allowing developers and AI agents to interact with it using common Unix tools such as ls, cat, find, and grep, rather than via APIs or SDKs. By Renato Losio
Day 61 of #100DaysOfCode — Python Refresher Part 1
When I started my #100DaysOfCode journey, I began with frontend development using React, then moved into backend development with Node.js and Express. After that, I explored databases to understand how data is stored and managed, followed by building full-stack applications with Next.js. It is now time to start learning Python, not from scratch, but as a refresher to strengthen my fundamentals and expand my backend skillset. Learning Python strengthens my core programming skills and offers a new perspective beyond JavaScript. It aligns with backend development, data handling, and automation, allowing me to build on my existing knowledge and become a more versatile developer. Today, for Day 61, I focused on revisiting the core building blocks of Python. Core Syntax Variables Data Types Vari
OpenClaw 2026.3.31: Task Flows, Locked-Down Installs, and the Security Release Your Agent Needed
OpenClaw 2026.3.31 dropped yesterday, and this one's different. Where the last few releases added capabilities — new channels, new models, new tools — this release is about control . Specifically: controlling what your agent installs, what your nodes can access, and how background work is tracked. If you run agents in production, this is the update you've been waiting for. Task Flows: Your Agent's Work Finally Has a Paper Trail This is the headline feature and it's been a long time coming. Background tasks — sub-agents, cron jobs, ACP sessions, CLI background runs — were all tracked separately. Different systems, different lifecycle management, different ways things could silently break. Not anymore. Everything now lives under one SQLite-backed ledger . You can run openclaw flows list , op
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!