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

TL;DR

AP-CAP addresses the bottleneck of scarce high quality data for 2D animal pose estimation by introducing a diffusion based controllable image generation pipeline. It combines a Multi-Modal Animal Image Generation Model with three data synthesis strategies MF-AISS, PA-AISS and CE-AISS to generate pose annotated images and create MPCH, the first large-scale hybrid synthetic-real dataset for animal pose estimation. The diffusion-based generator is trained end-to-end and leveraged to improve both in-domain performance and cross-domain generalization across diverse species. The approach achieves consistent gains across multiple backbones and datasets, and the MPCH benchmark provides a new, scalable resource for evaluating animal pose estimation in cross-domain settings.

Abstract

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.

Figures (9)

• Figure 1: Difference between traditional Animal Image Generation Paradigm and Ours. Top: Traditional 3D Modeling & Color Rendering Pipeline. Bottom: Our End-to-End Controllable Animal Image Generation Pipeline.
• Figure 2: Controllable Image Generation Pipeline, consisting of three strategies: Modality-Fusion-Based Animal Image Synthesis Strategy (MF-AISS), Pose-Adjustment-Based Animal Image Synthesis Strategy (PA-AISS), and Caption-Enhancement-Based Animal Image Synthesis Strategy(PA-AISS).
• Figure 3: (a) Synthetic data generation guided by input images and pose maps using CFLD lu2024coarse. (b) Synthetic data generation controlled by text and pose maps using ControlNet zhang2023adding and our method MF-AISS. (c) Real data.
• Figure 4: Cross-domain data synthesis based on the MF-AISS strategy. Red dashed: AnimalPose; Yellow: AP10K; Green: Animal Kingdom-Birds. Column 1 shows real samples, with subsequent columns displaying generated results.
• Figure 5: Generated images of the MF-AISS, PA-AISS, and CE-AISS strategies.