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

2025-04-01

AI Summary

To address the scarcity of high-quality annotated data for 2D animal pose estimation, this paper proposes a controllable multimodal image generation framework. Methodologically, we introduce the first modality-pose-caption-heterogeneous (MPCH) dataset, integrating visual modalities, pose annotations, and descriptive text; design three synthetic strategies—multimodal fusion, dynamic pose adjustment, and text-guided editing; and incorporate cross-modal feature alignment with controllable diffusion-based generation. Our contributions include: (1) constructing MPCH, the largest heterogeneous animal pose benchmark to date; (2) enabling on-demand synthesis of high-fidelity images spanning diverse poses and species; and (3) substantially improving downstream pose estimators across multiple animal categories—achieving a +12.6% average precision (AP) gain in keypoint detection and markedly enhancing cross-species generalization performance.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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.

Problem

• Addressing scarcity of high-quality datasets for animal pose estimation
• Proposing controllable pipeline for synthetic animal pose data generation
• Enhancing pose estimator performance with multi-modal hybrid dataset

Innovation

• Multi-Modal Animal Image Generation Model
• Modality-Fusion-Based Image Synthesis Strategy
• Pose-Adjustment-Based Image Synthesis Strategy