Abstract
Data augmentation is crucial for improving the robustness of face detection systems, especially under challenging conditions such as occlusion, illumination variation, and complex environments. Traditional copy paste augmentation often produces unrealistic composites due to inaccurate foreground extraction, inconsistent scene geometry, and mismatched background semantics. To address these limitations, we propose Depth Copy Paste, a multimodal and depth aware augmentation framework that generates diverse and physically consistent face detection training samples by copying full body person instances and pasting them into semantically compatible scenes. Our approach first employs BLIP and CLIP to jointly assess semantic and visual coherence, enabling automatic retrieval of the most suitable background images for the given foreground person. To ensure high quality foreground masks that preserve facial details, we integrate SAM3 for precise segmentation and Depth-Anything to extract only the non occluded visible person regions, preventing corrupted facial textures from being used in augmentation. For geometric realism, we introduce a depth guided sliding window placement mechanism that searches over the background depth map to identify paste locations with optimal depth continuity and scale alignment. The resulting composites exhibit natural depth relationships and improved visual plausibility. Extensive experiments show that Depth Copy Paste provides more diverse and realistic training data, leading to significant performance improvements in downstream face detection tasks compared with traditional copy paste and depth free augmentation methods.
Abstract (translated)
数据增强对于提升面部检测系统在遮挡、光照变化和复杂环境等挑战条件下的鲁棒性至关重要。传统的复制粘贴数据增强方法由于前景提取不准确、场景几何不一致以及背景语义不匹配等问题，常常生成不符合现实的合成图像。为了解决这些问题，我们提出了Depth Copy Paste（深度复制粘贴），这是一种多模态且感知深度的数据增强框架，通过将完整身体的人体实例复制并粘贴到语义兼容的场景中来生成多样且物理上一致的面部检测训练样本。 我们的方法首先利用BLIP和CLIP联合评估语义和视觉一致性，从而自动检索最适合给定前景人体背景图像。为了保证高质量的前景遮罩以保留面部细节，我们整合了SAM3进行精确分割，并使用Depth-Anything提取仅限于未被遮挡且可见的人体区域，避免在数据增强过程中使用受损的面部纹理。 为确保几何真实感，我们引入了一种基于深度引导的滑动窗口放置机制，在背景深度图上搜索具有最优深度连续性和尺度对齐的粘贴位置。这样生成的合成图像展现出自然的深度关系和改进了的视觉可信度。 通过广泛的实验表明，Depth Copy Paste能够提供更加多样且真实的训练数据，相比传统的复制粘贴方法及不依赖于深度的方法，在下游面部检测任务中带来了显著的性能提升。
URL
https://arxiv.org/abs/2512.11683
