Hand pose estimation plays a vital role in capturing subtle nonverbal cues essential for understanding human affect. However, collecting diverse, expressive real-world data remains challenging due to labor-intensive manual annotation that often underrepresents demographic diversity and natural expressions. To address this issue, we introduce a cost-effective approach to generating synthetic data using high-fidelity 3D hand models and a wide range of affective hand poses. Our method includes varied skin tones, genders, dynamic environments, realistic lighting conditions, and diverse naturally occurring gesture animations. The resulting dataset, Hi5, contains 583,000 pose-annotated images, carefully balanced to reflect natural diversity and emotional expressiveness. Models trained exclusively on Hi5 achieve performance comparable to human-annotated datasets, exhibiting superior robustness to occlusions and consistent accuracy across diverse skin tones -- which is crucial for reliably recognizing expressive gestures in affective computing applications. Our results demonstrate that synthetic data effectively addresses critical limitations of existing datasets, enabling more inclusive, expressive, and reliable gesture recognition systems while achieving competitive performance in pose estimation benchmarks. The Hi5 dataset, data synthesis pipeline, source code, and game engine project are publicly released to support further research in synthetic hand-gesture applications.

翻译：手部姿态估计在捕捉理解人类情感所必需的微妙非语言线索方面起着至关重要的作用。然而，由于劳动密集型的人工标注往往难以充分体现人口统计多样性和自然表情，收集多样化、富有表现力的真实世界数据仍然具有挑战性。为解决这一问题，我们引入了一种经济高效的方法，利用高保真3D手部模型和广泛的情感化手部姿态来生成合成数据。我们的方法涵盖了不同的肤色、性别、动态环境、真实光照条件以及多样化的自然手势动画。由此产生的数据集Hi5包含583,000张带有姿态标注的图像，经过精心平衡以反映自然多样性和情感表现力。仅使用Hi5训练出的模型，其性能可与人工标注数据集相媲美，并在遮挡条件下表现出卓越的鲁棒性，且在不同肤色上保持一致的准确性——这对于在情感计算应用中可靠识别富有表现力的手势至关重要。我们的结果表明，合成数据能有效解决现有数据集的关键局限性，在姿态估计基准测试中实现竞争力的同时，能够构建更具包容性、表现力和可靠性的手势识别系统。Hi5数据集、数据合成流程、源代码及游戏引擎项目均已公开发布，以支持合成手势应用的进一步研究。