外观
论文HF Daily Papers · arXiv 2610.330797 天前
面向具身训练视频存储优化：DDRA编码实现1.45倍加载吞吐提升
本文由 AI 根据公开内容整理，可能有误，请以原文为准：Rethinking Video Storage for AI Training: Breaking Cross-Frame Dependencies for Efficient Random Access（HF Daily Papers · arXiv 2610.33079）
核心内容
针对大规模具身智能训练中视频数据随机时序采样与现有顺序播放存储范式的矛盾，研究团队提出协同设计解码调度与视频参考结构的新方法。该方法旨在解决独立JPEG帧或短GOP方案导致的存储膨胀问题，通过消除无关帧重建开销，显著提升数据加载效率。
技术方案包含依赖感知稀疏解码与离线DDRA编码：前者仅重建目标帧及其必要参考链；后者将内部帧参考限制为两个锚点，使单目标重建上限锁定为3帧且独立于I帧周期。在大规模机器人语料库实验中，该方案在保持重建质量前提下，加载吞吐量达到LeRobot v3的1.45倍，媒体传输负载减少58.7%。
关键看点
- DDRA编码机制：采用离线依赖解耦随机访问编码，将内部帧参考限制为两个边界帧内编码锚点，确保每个采样目标的重建帧数上限为3帧，彻底解耦I帧周期对随机读取性能的限制。
- 稀疏解码策略：实施依赖感知稀疏解码，精准识别并仅重建请求帧及其必需参考链，跳过所有无关帧的重建过程，无需对源视频进行转码即可消除顺序重构带来的计算放大效应。
- 实测性能指标：在大规模机器人数据集验证中，相比LeRobot v3基准，新方法在同等重建质量下实现1.45倍加载吞吐提升，同时媒体有效负载降低58.7%，显著优化IO带宽利用率。
工程与产业观察
该工作揭示了具身智能数据基础设施的关键瓶颈：传统视频压缩的时序依赖性严重制约训练IO效率。DDRA方案通过重构编码参考结构，在不牺牲压缩率前提下实现了高效的随机访问，为构建PB级具身视频数据集提供了底层存储范式参考，直接加速仿真到真机的数据闭环迭代。
英文摘要 (Abstract)
Large-scale AI training increasingly relies on massive video datasets with random temporal sampling patterns. However, existing video storage paradigms are optimized for sequential playback and employ sophisticated inter-frame reference structures to maximize compression efficiency. When applied to the random-sampling workloads of AI training, these paradigms face substantial decoding-efficiency bottlenecks. Workarounds such as independent JPEG frames and short groups of pictures (GOPs) reduce decoding work at the cost of larger storage and transfer volumes. In this paper, we identify two sources of decode amplification: sequential reconstruction of unrelated frames and reconstruction of the reference chains required by sampled targets. We address both sources by co-designing the decoding schedule and video reference structure. Dependency-aware sparse decoding reconstructs only requested frames and their required references, skipping unrelated reconstruction without transcoding the source videos. Optional offline Dependency-Decoupled Random Access (DDRA) encoding further reduces reference reconstruction by restricting each interior frame to two bounding intra-coded anchors. Together, sparse decoding and DDRA retain inter-frame prediction while bounding reconstruction to at most three frames per requested target, independent of the intra period. Experiments on a large-scale robotics corpus show that, at comparable reconstruction quality, our method achieves 1.45times the loading throughput of LeRobot v3 with 58.7% less media payload.
Hugging Face Daily Papers 热度：8 upvotes 作者：Zetian Song, Qize Yu, YueChen, Bohan Zhou, Bowen Ping, Yuqi Ye, Haocheng Tang, Haotian Zhang, Qizhe Wang, Shiyu Huang, Jiaqi Zhang, Siwei Ma, Wen Gao
来源
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