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ACM MM2025顶会

Gaze-Adaptive Foveation for Remote Rendered VR

Adhi Widagdo, Teemu Kämäräinen, Ahmad Alhilal, Matti Siekkinen, Cheng-Hsin Hsu

2025年份

摘要

Remote rendering enables high-fidelity virtual reality (VR) experiences on standalone headsets by offloading intensive graphics workloads to remote servers. However, streaming high-quality VR graphics imposes substantial bandwidth and latency challenges. Spatial compression is a form of foveation which addresses this challenge by leveraging the human visual system's varying acuity, allocating higher visual quality around the user's gaze while reducing resolution in the periphery. In this work, we implement three gaze-adaptive foveation methods: Dynamic Axis-Aligned Distortion Transmission (D-AADT2 and D-AADT3) and Dynamic Foveated Radial Warp (D-FRW)) of which only D-AADT2 has been previously presented. These methods dynamically adapt spatial compression based on gaze-tracking input, ensuring optimal perceptual quality. We integrate these methods together with their static counterparts into the open-source Air Light VR (ALVR) remote-rendering framework, enabling native (72 FPS) framerates. We conclude a comprehensive objective evaluation across diverse VR games and demonstrate that the dynamic methods significantly outperform traditional static approaches in both encoding efficiency and perceptual quality metrics. A complementary subjective user study further validates these findings, confirming that dynamic gaze-adaptive foveation substantially enhances visual quality, immersion, and user interaction experience.

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