BOXR: Body and head motion Optimization framework for eXtended Reality
Ziliang Zhang, Zexin Li, Hyoseung Kim, Cong Liu
摘要
The emergence of standalone Extended Reality (XR) systems has enhanced user mobility, accommodating both subtle, frequent head motions and substantial, less frequent body motions. However, the pervasively used Motion-to-Display (M2D) latency metric, which measures the delay between the most recent motion and its corresponding display update, only accounts for head motions. This oversight can leave users prone to motion sickness if significant body motion is involved. Although existing methods optimize M2D latency through asynchronous task scheduling and reprojection methods, they introduce challenges like resource contention between tasks and outdated pose data. These challenges are further complicated by user motion dynamics and scene changes during runtime. To address these issues, we for the first time introduce the Camera-to-Display (C2D) latency metric, which captures the delay caused by body motions, and present BOXR, a framework designed to co-optimize both body and head motion delays within an XR system. BOXR enhances the coordination between M2D and C2D latencies by efficiently scheduling tasks to avoid contentions while maintaining an up-to-date pose in the output frame. Moreover, BOXR incorporates a motion-driven visual inertial odometer to adjust to user motion dynamics and employs scene-dependent foveated rendering to manage changes in the scene effectively. Our evaluations show that BOXR significantly outperforms state-of-the-art solutions in 11 EuRoC MAV datasets across 4 XR applications across 3 hardware platforms. In controlled motion and scene settings, BOXR reduces M2D and C2D latencies by up to and , respectively and increases frame rate by up to . In practical deployments, BOXR achieves substantial reductions in real-world scenarios-up to in M2D latency and in C2D latency-while maintaining remarkably low miss rates of only for M2D requirements and for C2D requirements.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper2
- Process Only Where You Look: Hardware and Algorithm Co-optimization for Efficient Gaze-Tracked Foveated Rendering in Virtual RealityHaiyu Wang, Wenxuan Liu, Kenneth Chen, Qi Sun 等ISCA 2025 · 被引用 6 次
- Foveated Instance SegmentationHongyi Zeng, Wenxuan Liu, Tianhua Xia, Jinhui Chen 等CVPR 2025
它引用的顶会 Paper7
- Coterie: Exploiting Frame Similarity to Enable High-Quality Multiplayer VR on Commodity Mobile DevicesJiayi Meng, Sibendu Paul, Y. Charlie HuASPLOS 2020 · 被引用 60 次
- Firefly: Untethered Multi-user VR for Commodity Mobile DevicesXing Liu, Christina Vlachou, Feng Qian, Chendong Wang 等USENIX ATC 2020 · 被引用 46 次
- Q-VR: system-level design for future mobile collaborative virtual realityChenhao Xie, Xie Li, Yang Hu, Huwan Peng 等ASPLOS 2021 · 被引用 36 次
- Power, Performance, and Image Quality Tradeoffs in Foveated RenderingRahul Singh, Muhammad Huzaifa, Jeffrey Liu, Anjul Patney 等IEEE VR 2023 · 被引用 31 次
- Balancing Energy Efficiency and Real-Time Performance in GPU SchedulingYidi Wang, Mohsen Karimi, Yecheng Xiang, Hyoseung KimRTSS 2021 · 被引用 29 次
相关 Paper
- Simultaneous Run-Time Measurement of Motion-to-Photon Latency and Latency JitterJan-Philipp Stauffert, Florian Niebling, Marc Erich LatoschikIEEE VR 2020 · 被引用 6 次
- Measuring System Visual Latency through Cognitive Latency on Video See-Through AR devicesRobert Gruen, Eyal Ofek, Anthony Steed, Ran Gal 等IEEE VR 2020 · 被引用 7 次
- Integrating Both Parallax and Latency Compensation into Video See-through Head-mounted DisplayAtsushi Ishihara, Hiroyuki Aga, Yasuko Ishihara, Hirotake Ichikawa 等IEEE VR 2023 · 被引用 16 次
- A Two-Millisecond Passthrough Headset for Perceptual StudiesEric Penner, Josephine D'Angelo, Clinton Smith, Nathan Matsuda 等SIGGRAPH 2026
- Berkeley Open Extended Reality Recordings 2023 (BOXRR-23): 4.7 Million Motion Capture Recordings from 105,000 XR UsersVivek Nair, Wenbo Guo, Rui Wang, James F. O'Brien 等IEEE VR 2024 · 被引用 17 次
