针对截肢人群的3D人体建模，提升重建精度。
AJAHR: Amputated Joint Aware 3D Human Mesh Recovery
- 引入截肢检测模块，联合训练优化人体网格重建
- 在非截肢者上保持竞争力，在截肢者上达到新高
- 构建合成数据集A3D，覆盖多种截肢姿态
现有3D人体网格恢复方法假设标准人体结构，忽略截肢等异常解剖情况，导致对截肢人群存在偏差，且因缺乏合适数据集而加剧。为此，我们提出截肢关节感知的3D人体网格恢复（AJAHR），一种自适应姿态估计框架，可提升截肢个体的网格重建效果。模型集成一个体部截肢分类器，与网格恢复网络联合训练以检测潜在截肢。同时引入合成数据集Amputee 3D（A3D），涵盖广泛截肢姿态，支持鲁棒训练。在非截肢者上保持竞争力，在截肢者上达到当前最优表现。
原文摘要 · Abstract (English)
Existing human mesh recovery methods assume a standard human body structure, overlooking diverse anatomical conditions such as limb loss. This assumption introduces bias when applied to individuals with amputations - a limitation further exacerbated by the scarcity of suitable datasets. To address this gap, we propose Amputated Joint Aware 3D Human Mesh Recovery (AJAHR), which is an adaptive pose estimation framework that improves mesh reconstruction for individuals with limb loss. Our model integrates a body-part amputation classifier, jointly trained with the mesh recovery network, to detect potential amputations. We also introduce Amputee 3D (A3D), which is a synthetic dataset offering a wide range of amputee poses for robust training. While maintaining competitive performance on non-amputees, our approach achieves state-of-the-art results for amputated individuals. Additional materials can be found at the project webpage.
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