In this paper, we propose to address monocular 3D hand pose estimation from a single RGB or depth image via articulated anchor-to-joint 3D local regressors, in form of A2J-Transformer+. The key idea is to make the local regressors (i.e., anchor points) in 3D space be aware of hand's local fine details and global articulated context jointly, to facilitate predicting their 3D offsets toward hand joints with linear weighted aggregation for joint localization. Our intuition is that, local fine details help to estimate accurate offset but may suffer from the issues including serious occlusion, confusing similar patterns, and overfitting risk. On the other hand, hand's global articulated context can essentially provide additional descriptive clues and constraints to alleviate these issues. To set anchor points adaptively in 3D space, A2J-Transformer+ runs in a 2-stage manner. At the first stage, since the input modality property anchor points distribute more densely on X-Y plane, it leads to lower prediction accuracy along Z direction compared with those in the X and Y directions. To alleviate this, at the second stage anchor points are set near the joints yielded by the first stage evenly along X, Y, and Z directions. This treatment brings two main advantages: (1) balancing the prediction accuracy along X, Y, and Z directions, and (2) ensuring the anchor-joint offsets are of small values relatively easy to estimate. Wide-range experiments on three RGB hand datasets (InterHand2.6 M, HO-3D V2 and RHP) and three depth hand datasets (NYU, ICVL and HANDS 2017) verify A2J-Transformer+'s superiority and generalization ability for different modalities (i.e., RGB and depth) and hand cases (i.e., single hand, interacting hands, and hand-object interaction), even outperforming model-based manners. The test on ITOP dataset reveals that, A2J-Transformer+ can also be applied to 3D human pose estimation task.

3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition. Our key idea is to equip A2J with strong local-global aware ability to well capture interacting hands' local fine details and global articulated clues among joints jointly. To this end, A2J is evolved under Transformer's non-local encoding-decoding framework to build A2J- Transformer. It holds 3 main advantages over A2J. First, self-attention across local anchor points is built to make them global spatial context aware to better capture joints' articulation clues for resisting occlusion. Secondly, each anchor point is regarded as learnable query with adaptive feature learning for facilitating pattern fitting capacity, instead of having the same local representation with the others. Last but not least, anchor point locates in 3D space instead of 2D as in A2J, to leverage 3D pose prediction. Experiments on challenging InterHand 2.6M demonstrate that, A2J-Transformer can achieve state-of-the-art model-free performance (3.38mm MPJPE advancement in 2-hand case) and can also be applied to depth domain with strong generalization. The code is avaliable at https://github.com/ChanglongJiangGit/A2J-Transformer.