AI 中文总结
提出Astrolabe适配器，通过球面图转换为噪声偏移引导扩散管道，在Puzzle-IOI和4D-Dress数据集上提升全身捕获的图像与几何指标，后视图图像增益显著。
AI 中文摘要
从非约束照片中进行全身捕获需要在任意视角、姿态、裁剪和遮挡情况下建立全局对应关系。然而，在此场景下估计的姿态、几何形状和基础特征对于密集匹配或外观迁移来说过于不可靠，而扩散校正器和优化管道并未提供通用接口来利用这种不确定的对应关系。我们的见解是，对应关系无需局部准确：其粗略的视角和身体布局仍可组织扩散先验的自适应方式并引导重建。我们引入Astrolabe，这是一种基于冻结视角引导球面图（SPH）的主机可移植适配器。固定有界变换将SPH转换为空间噪声偏移，该偏移在先前自适应期间匹配，并在两类管道的下游去噪或分数蒸馏引导期间复用。当校正器暴露参考路由器时，相同的目标/参考SPH还提供粗略兼容性分数以选择原生外观特征；无路由器的优化仅使用共享偏移路径。因此，Astrolabe遵循SPH-偏移-自适应-引导的流程，无需密集变形或学习的控制分支。在Puzzle-IOI和4D-Dress数据集上，它在两类主机中均提升了所有报告的图像指标，在配对的Puzzle-IOI几何指标中也全部提升；图像增益扩展至后视图，而4D-Dress的几何整体保持稳定。
英文摘要
Full-body capture from unconstrained photographs requires global correspondence across arbitrary views, poses, crops, and occlusions. Yet pose, geometry, and foundation features estimated in this setting are too unreliable for dense matching or appearance transfer, while diffusion rectifiers and optimization pipelines expose no common interface for consuming such uncertain correspondence. Our insight is that correspondence need not be locally accurate: its coarse viewpoint and body layout can still organize how a diffusion prior adapts and guides reconstruction. We introduce \emph{Astrolabe}, a host-portable adapter built on frozen viewpoint-guided spherical maps (SPH). A fixed bounded transform converts SPH into a spatial noise shift, which is matched during prior adaptation and reused during downstream denoising or score-distillation guidance in both pipeline categories. When a rectifier exposes a reference router, the same target/reference SPH additionally supplies coarse compatibility scores to select native appearance features; router-free optimization uses only the shared shift path. Astrolabe therefore follows one SPH--shift--adapt--guide process without dense warping or a learned control branch. Across Puzzle-IOI and 4D-Dress, it improves all reported image metrics in both hosts and all paired Puzzle-IOI geometry metrics; image gains extend to rear views, while 4D-Dress geometry remains stable overall.
