解决低光人体姿态估计中的图像失真与视觉线索失效问题
UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation
- 用高通滤波和光照特征注入模块合成更真实的低光图像
- 在硬测试集上提升10.1的AP（56.4%相对增益）
- 适合做低光场景下人体姿态估计的研究者和开发者
低光照等低可见性场景因缺乏标注数据且视觉信息丢失，给人体姿态估计带来挑战。现有领域自适应方法通过增强亮光图像模拟低光条件，但手工增强会简化噪声模式，学习型方法常无法保留高频低光特征，生成不真实图像导致模型泛化能力差。此外，当前姿态估计算法依赖图像到关键点的交叉注意力，但在低光下该线索不可靠。为此，我们提出无监督领域自适应姿态估计框架UDAPose，通过引入基于直流分量的高通滤波器（DHF）和低光特征注入模块（LCIM），从输入低光图像中注入高频细节，克服现有方法刚性或细节丢失的问题。同时设计动态注意力控制模块（DCA），在Transformer架构中自适应平衡图像线索与学习到的姿态先验。实验表明，UDAPose显著优于现有方法，在ExLPose-test硬集（LL-H）上取得10.1的AP提升（相对增益56.4%），在跨数据集验证EHPT-XC上提升7.4（31.4%）。代码已开源。
原文摘要 · Abstract (English)
Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-light datasets and the loss of visual information under poor illumination. Recent domain adaptation techniques attempt to utilize well-lit labels by augmenting well-lit images to mimic low-light conditions. But handcrafted augmentations oversimplify noise patterns, while learning-based methods often fail to preserve high-frequency low-light characteristics, producing unrealistic images that lead pose models to generalize poorly to real low-light scenes. Moreover, recent pose estimators rely on image cues through image-to-keypoint cross-attention, but these cues become unreliable under low-light conditions. To address these issues, we propose Unsupervised Domain Adaptation for Pose Estimation (UDAPose), a novel framework that synthesizes low-light images and dynamically fuses visual cues with pose priors for improved pose estimation. Specifically, our synthesis method incorporates a Direct-Current-based High-Pass Filter (DHF) and a Low-light Characteristics Injection Module (LCIM) to inject high-frequency details from input low-light images, overcoming rigidity or the detail loss in existing approaches. Furthermore, we introduce a Dynamic Control of Attention (DCA) module that adaptively balances image cues with learned pose priors in the Transformer architecture. Experiments show that UDAPose outperforms state-of-the-art methods, with notable AP gains of 10.1 (56.4%) on the ExLPose-test hard set (LL-H) and 7.4 (31.4%) in cross-dataset validation on EHPT-XC. Code: https://github.com/Vision-and-Multimodal-Intelligence-Lab/UDAPose
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