Meaning over Motion: A Semantic-First Approach to 360° Viewport Prediction

2026-01-08

arXiv.org

AI Summary

This work addresses the limitations of existing 360-degree video viewport prediction methods, which rely on motion or low-level saliency and struggle with semantic-driven rapid saccades—termed the “Saccade Trap”—leading to playback stalls during high user engagement. To overcome this, we propose a semantics-first viewport prediction framework that generates lightweight semantic association maps on the server to guide a client-side low-latency controller in dynamically adjusting prefetching strategies. During gaze stabilization periods, our approach enhances bandwidth efficiency and proactively preloads semantically relevant regions. By innovatively integrating cognitive intent into network control through architectural inversion and an association-based lookahead mechanism, we transform stable fixation intervals into proactive interaction preparation phases. Combined with semantic-adaptive conformal tiling, multimodal prediction sets, and lightweight collaborative inference, our method reduces stall duration by at least 20% and decreases effective bandwidth consumption by at least 18% compared to state-of-the-art baselines on the 360-AV-HM dataset.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Consent frameworks and practices on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Ultra-high-resolution 360-degree video streaming is severely constrained by the massive bandwidth required to deliver immersive experiences. Current viewport prediction techniques predominately rely on kinematics or low-level visual saliency, treating users as passive physical objects governed by inertia. This theoretical limitation leads to the"Saccade Trap"-- a critical failure mode where predictors fail to anticipate rapid, meaning-driven shifts in attention, causing rebuffering stalls exactly when user engagement is highest. To resolve this, we propose Semantically-Adaptive Conformal Tiling with Associative Lookahead, a novel framework that integrates cognitive intent into network control. Unlike"one-size-fits-all"approaches, our method utilizes an architectural inversion strategy: heavy semantic reasoning is offloaded to the server to generate lightweight association graphs, which guide a low-latency client-side controller. We construct a personalized Multi-Modal Prediction Set that dynamically tightens safety margins during stable fixation to maximize efficiency, while simultaneously pre-fetching non-adjacent tiles containing semantically linked objects (Associative Lookahead). This mechanism effectively converts the"calm"of fixation into a preparation phase for the next interaction. Trace-driven evaluation on the 360-AV-HM dataset demonstrates that this approach successfully mitigates the Saccade Trap, reducing stall duration by $\ge$ 20% and lowering effective bandwidth consumption by $\ge$ 18% compared to state-of-the-art trajectory-based baselines.

Problem

• viewport prediction
• 360-degree video
• saccade trap
• semantic attention
• bandwidth efficiency

Innovation

• Semantic-first viewport prediction
• Associative Lookahead
• Conformal tiling
• Saccade Trap
• Multi-Modal Prediction Set