Monocular vertex-level human-scene contact prediction is a fundamental capability for interactive systems such as assistive monitoring, embodied AI, and rehabilitation analysis. In this work, we study this task jointly with single-image 3D human mesh reconstruction, using reconstructed body geometry as a scaffold for contact reasoning. Existing approaches either focus on contact prediction without sufficiently exploiting explicit 3D human priors, or emphasize pose/mesh reconstruction without directly optimizing robust vertex-level contact inference under occlusion and perceptual noise. To address this gap, we propose GraphiContact, a pose-aware framework that transfers complementary human priors from two pretrained Transformer encoders and predicts per-vertex human-scene contact on the reconstructed mesh. To improve robustness in real-world scenarios, we further introduce a Single-Image Multi-Infer Uncertainty (SIMU) training strategy with token-level adaptive routing, which simulates occlusion and noisy observations during training while preserving efficient single-branch inference at test time. Experiments on five benchmark datasets show that GraphiContact achieves consistent gains on both contact prediction and 3D human reconstruction. Our code, based on the GraphiContact method, provides comprehensive 3D human reconstruction and interaction analysis, and will be publicly available at https://github.com/Aveiro-Lin/GraphiContact.

翻译：单目顶点级人体-场景接触预测是辅助监控、具身智能及康复分析等交互系统的核心能力。本文联合研究该任务与单图像三维人体网格重建，以重建的体态几何作为接触推理的支撑框架。现有方法或未充分挖掘显式三维人体先验进行接触预测，或侧重姿态/网格重建而缺乏对遮挡及感知噪声下稳健顶点级接触推理的直接优化。为弥补这一不足，我们提出GraphiContact——一种姿态感知框架，该框架从两个预训练Transformer编码器中迁移互补的人体先验，并在重建网格上逐顶点预测人体-场景接触。为提升真实场景的鲁棒性，我们进一步引入带令牌级自适应路由的单图像多推断不确定性训练策略：该策略在训练时模拟遮挡与噪声观测，同时保持测试阶段高效的单分支推理。在五个基准数据集上的实验表明，GraphiContact在接触预测和三维人体重建任务上均取得一致性提升。基于GraphiContact方法的代码提供了完整的三维人体重建与交互分析功能，将开源发布于https://github.com/Aveiro-Lin/GraphiContact。