Efficient 3D Bird Pose Estimation via Gated Large-Kernel Attention and Unsupervised Geometric Constraints

3D bird pose estimation plays a pivotal role in ecological conservation research. However, it remains a formidable challenge due to extensive joint deformation, severe self-occlusion, and the scarcity of 3D ground truth data. Therefore, practical solutions typically rely on accurate 2D keypoint detection from monocular images and subsequent 3D lifting. Although the High-Resolution Network (HRNet) has established a benchmark in 2D pose estimation by preserving high-resolution feature representations, its architecture, which relies on small convolution kernels, faces difficulties in capturing the global long-range dependencies necessary to resolve severe occlusions. To address these deficiencies, the core contributions of this work are summarized as follows: (1) We design a Gated LS-Block with a partial channel gating strategy to decouple channel mixing from spatial mixing, and extract global long-range dependencies via the proposed Large–Small Convolution (LSConv) to minimize feature redundancy. (2) We embed this block into Stage 2 of HRNet, enhancing multi-scale feature learning while slightly reducing model parameters and computational overhead; (3) To alleviate the ill-posed nature of monocular 3D lifting without paired supervision, we develop an unsupervised 3D reconstruction algorithm. Experimental results on the Animal Kingdom dataset demonstrate that our method achieves a 0.9% improvement in PCK@0.05 while reducing GFLOPs by 3.3%. These results verify that the proposed architecture enhances the model’s representation capability for bird poses while ensuring efficient inference. Meanwhile, we validate the applicability of the proposed 3D reconstruction algorithm via qualitative experiments, and further demonstrate that our unsupervised 3D lifting algorithm successfully preserves low symmetry error and robust bone length consistency with proxy metrics.