标题： AP-CAP：通过可控图像生成管道推进高质量动物姿态估计的数据合成 显示英文标题

标题： AP-CAP: Advancing High-Quality Data Synthesis for Animal Pose Estimation via a Controllable Image Generation Pipeline

摘要： 二维动物姿态估计任务在推动动物行为分析和生态学研究中的深度学习应用方面发挥着至关重要的作用。尽管现有的一些方法取得了显著进展，但我们的研究表明，高质量数据集的匮乏仍然是一个重要的瓶颈，限制了当前方法的全部潜力。 为了解决这一挑战，我们提出了一种新颖的可控图像生成管道，用于合成动物姿态估计数据，称为AP-CAP（Animal Pose - Controlled AutoPipeline）。在这个管道中，我们引入了一个多模态动物图像生成模型，能够生成具有预期姿态的图像。 为了提高生成数据的质量和多样性，我们进一步提出了三种创新策略：（1）基于模态融合的动物图像合成策略，以整合多源外观表示；（2）基于姿态调整的动物图像合成策略，以动态捕捉多样化的姿态变化；（3）基于标题增强的动物图像合成策略，以丰富视觉语义理解。 利用所提出的模型和策略，我们创建了MPCH数据集（模态-姿态-标题混合），这是第一个创新性地结合合成和真实数据的混合数据集，建立了迄今为止最大规模的多源异构基准库，用于动物姿态估计。 广泛的实验表明，我们的方法在提高动物姿态估计器的性能和泛化能力方面具有优越性。 显示英文摘要

摘要： The task of 2D animal pose estimation plays a crucial role in advancing deep learning applications in animal behavior analysis and ecological research. Despite notable progress in some existing approaches, our study reveals that the scarcity of high-quality datasets remains a significant bottleneck, limiting the full potential of current methods. To address this challenge, we propose a novel Controllable Image Generation Pipeline for synthesizing animal pose estimation data, termed AP-CAP. Within this pipeline, we introduce a Multi-Modal Animal Image Generation Model capable of producing images with expected poses. To enhance the quality and diversity of the generated data, we further propose three innovative strategies: (1) Modality-Fusion-Based Animal Image Synthesis Strategy to integrate multi-source appearance representations, (2) Pose-Adjustment-Based Animal Image Synthesis Strategy to dynamically capture diverse pose variations, and (3) Caption-Enhancement-Based Animal Image Synthesis Strategy to enrich visual semantic understanding. Leveraging the proposed model and strategies, we create the MPCH Dataset (Modality-Pose-Caption Hybrid), the first hybrid dataset that innovatively combines synthetic and real data, establishing the largest-scale multi-source heterogeneous benchmark repository for animal pose estimation to date. Extensive experiments demonstrate the superiority of our method in improving both the performance and generalization capability of animal pose estimators.