从线稿图实现反向绑定器（Rig）优化
Inverse Rig Optimization from Line Drawings
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中文总结 AI 辅助
本文提出从屏幕空间轮廓笔触恢复绑定器参数的方法，用预训练MLP绑定器代理替代黑箱绑定器，实现从草图高效制作风格化3D角色动画关键帧，适用于多种角色与实际场景。
中文摘要 AI 辅助
风格化3D角色动画大多依赖手动制作，动画师需逐关键帧调整绑定器（Rig）参数以获得最佳姿态。由于风格化作品主要通过轮廓线呈现，在相机视图中绘制轮廓是表达艺术意图最直接精准的方式。绑定器控制与艺术家目标的不匹配导致了繁琐的试错流程，动画师需反复调整绑定器参数并对照渲染视图匹配所需轮廓。为解决该问题，本文提出一种从屏幕空间轮廓笔触恢复绑定器参数的方法，支持从草图实现高效关键帧制作。给定重绘当前轮廓的笔触，该方法优化DCC工具中定义的高级绑定器参数，核心是使用预训练的MLP绑定器代理，其提供从绑定器参数到网格顶点的可微映射，在优化过程中替代原本的黑箱绑定器。本文将用户绘制线条与网格轮廓线匹配，并通过代理反向传播产生的屏幕空间误差以更新绑定器参数。实验结果表明，该方法适用于多种角色及实际场景。
英文摘要
Stylized 3D character animation is largely hand-authored, with animators authoring rig parameters one keyframe at a time to find the best pose. Because stylized work reads chiefly through contour lines, drawing contours in the camera view is the most direct and precise way to express artistic intent. This mismatch between the rig controls and the artist's goal forces a laborious trial-and-error workflow, with animators repeatedly manipulating rig controls against the rendered view to match the desired contour. To address this, we propose a method that recovers rig parameters from screen-space contour strokes, enabling effective keyframing from sketches. Given strokes that redraw the current contour, our method optimizes the high-level rig parameters defined in the DCC tool. The key is to use a pre-trained MLP rig surrogate that provides a differentiable map from rig parameters to mesh vertices, replacing the original black-box rig within the optimization process. We match user-drawn lines to mesh contour lines and backpropagate the resulting screen-space error through the surrogate to update the rig parameters. Our results demonstrate that the method works for diverse characters and practical scenarios.
发表机构
- The University of Tokyo（东京大学）
机构由 AI 辅助整理，请以论文原文为准。
