An Asymmetric Dual-Stream Gated Transformer for 3D Human Pose Estimation

3D human pose estimation is a fundamental research task in computer vision, with the goal of predicting the 3D coordinates of key human joints to construct a kinematic skeleton. Given the robustness of existing 2D pose detectors, lifting multi-frame 2D keypoint sequences to 3D poses has become the dominant paradigm in the field, as this approach significantly improves estimation accuracy compared to processing single frames. Although the Transformer architecture excels at processing sequential data, existing models often lack physical priors of human structure, requiring vast amounts of data for learning and being prone to generating anatomically implausible poses. Furthermore, the global self-attention mechanism can introduce computational redundancy and noise interference. To address these issues, this work proposes a new model named ADSGFormer, which employs a novel dual-stream architecture to process spatial and temporal information in parallel. It leverages complementary information through a fusion mechanism to eliminate ambiguities, thereby enhancing the model’s representational capability and estimation accuracy. At the core of the model is an innovative temporal attention module that more effectively captures both global and local dependencies and effectively utilizes prior knowledge. Experimental results on Human3.6M and MPI-INF-3DHP show that ADSGFormer achieves competitive state-of-the-art performance without relying on multi-hypothesis sampling or diffusion-based post-processing.