用AI生成儿童姿势数据，不用真实照片就能训练高精度姿态识别模型。
Synthetic-Child: An AIGC-Based Synthetic Data Pipeline for Privacy-Preserving Child Posture Estimation
- 用3D人体模型生成带真实标注的儿童姿势图像，全程无需真人照片。
- 在真实测试集上准确率提升12.5点，量化后仍保持70.4 AP且实时运行。
- 适合隐私敏感场景，如儿童健康设备，也适用于其他需保护数据的领域。
准确的儿童姿势估计对AI学习助手设备至关重要，但收集大规模标注儿童数据既昂贵又涉及伦理风险。我们提出Synthetic-Child，一个基于AIGC的合成数据流水线，可生成带真实关键点标注的逼真儿童姿势图像，完全无需真实儿童照片。流程包含四步：(1) 在Blender中使用可编程3D儿童模型SMPL-X生成多样桌面学习姿势，保证解剖合理性并自动导出COCO格式标注；(2) 通过自定义PoseInjectorNode将3D骨架输入双控制网（姿态+深度）条件于FLUX-1 Dev，合成12,000张逼真图像，覆盖10类姿势，标注漂移低；(3) 采用ViTPose进行置信度过滤并针对性增强，提升鲁棒性；(4) 在合成数据上微调RTMPose-M（13.6M参数），结合几何特征工程与轻量MLP实现分类，并量化至INT8用于边缘部署。在真实儿童测试集（n~300）上，FP16模型达71.2 AP，较相同容量的COCO预训练成人数据基线提升12.5 AP。量化后仍保持70.4 AP，且在0.8-TOPS Rockchip RK3568 NPU上以22 FPS运行。单人对照实验中，相比商用姿势纠正器，本系统在多数类别上识别率更高，平均响应快1.8倍。结果表明，精心设计的AIGC流程可显著降低对真实儿童影像依赖，同时实现可部署的高精度，具有推广至其他隐私敏感领域的潜力。
原文摘要 · Abstract (English)
Accurate child posture estimation is critical for AI-powered study companion devices, yet collecting large-scale annotated datasets of children is both expensive and ethically prohibitive due to privacy concerns. We present Synthetic-Child, an AIGC-based synthetic data pipeline that produces photorealistic child posture training images with ground-truth-projected keypoint annotations, requiring zero real child photographs. The pipeline comprises four stages: (1) a programmable 3D child body model (SMPL-X) in Blender generates diverse desk-study poses with IK-constrained anatomical plausibility and automatic COCO-format ground-truth export; (2) a custom PoseInjectorNode feeds 3D-derived skeletons into a dual ControlNet (pose + depth) conditioned on FLUX-1 Dev, synthesizing 12,000 photorealistic images across 10 posture categories with low annotation drift; (3) ViTPose-based confidence filtering and targeted augmentation remove generation failures and improve robustness; (4) RTMPose-M (13.6M params) is fine-tuned on the synthetic data and paired with geometric feature engineering and a lightweight MLP for posture classification, then quantized to INT8 for real-time edge deployment. On a real-child test set (n~300), the FP16 model achieves 71.2 AP -- a +12.5 AP improvement over the COCO-pretrained adult-data baseline at identical model capacity. After INT8 quantization the model retains 70.4 AP while running at 22 FPS on a 0.8-TOPS Rockchip RK3568 NPU. In a single-subject controlled comparison with a commercial posture corrector, our system achieves substantially higher recognition rates across most tested categories and responds ~1.8x faster on average. These results demonstrate that carefully designed AIGC pipelines can substantially reduce dependence on real child imagery while achieving deployment-ready accuracy, with potential applications to other privacy-sensitive domains.
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