English(EN) Dual-stream Spatio-Temporal GCN-Transformer Network for 3D Human Pose Estimation

MixTGFormer 在三维人体姿态估计方面达到最先进水平

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-20 01:07

研究人员开发了一种名为 MixTGFormer 的新方法用于三维人体姿态估计，旨在改进现有的基于 Transformer 的方法。这种新颖的网络将图卷积网络 (GCN) 集成到其 Transformer 架构中，以更好地捕捉局部骨骼关系和全局时空动态。在 Human3.6M 和 MPI-INF-3DHP 基准数据集上的实验表明，MixTGFormer 取得了最先进的成果，优于其他方法。 AI

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-04-20 01:07

用于三维人体姿态估计的双流时空图卷积-Transformer网络

3D human pose estimation is a classic and important research direction in the field of computer vision. In recent years, Transformer-based methods have made significant progress in lifting 2D to 3D human pose estimation. However, these methods primarily focus on modeling global t…

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用于三维人体姿态估计的双流时空图卷积-Transformer网络

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