AI 中文总结
本文提出一种铜表面外观预测流程，采用闭式全局颜色外推方法实现跨试样外观预测，性能优于复制最后帧等方法，相关代码等资源已公开。
AI 中文摘要
数字设计需要预测金属表面氧化后的外观，本文提出了一种针对铜的相关流程。在固定相机观测的条件下，该系统可预测加速10个时间单位后的外观，并将其转换为渲染器所需的反照率、法线、粗糙度和金属度贴图。该预测作为创作工具使用时，在系统未观测过的铜试样上进行评估：保留完整的一段记录，因此训练和 checkpoint 选择使用一个试样，测试集为另一个在不同日期和条件下记录的完整试样。在该协议下，带有单调氧化状态的学习时空模型（单段记录内最准确的预测器）在未见过的试样上的预测准确性低于复制最后观测帧的方法，其他三种训练架构也存在同样情况。唯一可迁移的预测器是无训练参数的闭式全局颜色外推，相比复制最后帧的方法分别提升了13.4%和50.6%，且该优势随时间范围增大，在t+10时分别达到+16.7%和+55.5%。两项控制实验验证了该结果：对非氧化参考区域测量的光度漂移进行逐帧校正后，两个优势仍保持不变，排除了不受控曝光作为其来源；对每个记录包含的6个独立窗口进行移动块自举检验，结果显示较大的优势显著不为零，但较小的优势单独不显著。对机制的测量表明：学习到的敏感性图谱编码了训练试样上腐蚀开始的位置，在新试样上会产生误导，而全局颜色轨迹是所有试样共有的特征。因此，该流程对未见过的试样采用闭式预测器，对已观测过的试样则仅使用学习模型。代码、数据划分、协议和泄露审计均已公开。
英文摘要
Digital design requires predicting how a metal surface will look later in its oxidation; this paper presents such a pipeline for copper. Given a fixed-camera observation, the system forecasts appearance 10 accelerated units ahead and converts it into the albedo, normal, roughness and metallic maps a renderer consumes. Forecasting is evaluated as an authoring tool would use it, on a copper specimen the system has not observed: an entire recording is held out, so training and checkpoint selection use one specimen and the test set is the whole of a second, recorded on a different day and condition. Under this protocol a learned spatio-temporal model with a monotone oxidation state, the most accurate forecaster within a single recording, is less accurate than copying the last observed frame on an unseen specimen, in both directions, as are three further trained architectures. The only forecaster that transfers is a closed-form global color extrapolation with no trained parameters, improving on copy-last-frame by 13.4% and 50.6%, with a margin that increases with horizon to +16.7% and +55.5% at t+10. Two controls qualify this: correcting every frame for the photometric drift measured on a non-oxidizing reference region leaves both margins intact, ruling out uncontrolled exposure as their source, and a moving-block bootstrap over the 6 independent windows each recording contains separates the larger margin from zero but leaves the smaller one not individually significant. The mechanism is measured: a learned susceptibility map encodes where corrosion begins on the training specimen and misleads on a new one, whereas the global color trajectory is what specimens share. The pipeline therefore deploys the closed-form forecaster for unseen specimens and the learned model only for continuing one already observed. Code, splits, protocol and leakage audit are released.
Comments10 pages, 5 figures, 3 tables. (Repository https://github.com/RuffLogix/kstep-copper-forecast)
