This value depends on the sequence position of each input and provides the model with a notion of order that it can learn. The original transformer architecture uses a fixed positional encoding, PEPE, which provides a real scalar per-input embedding dimension at position j up to a predefined ...
The (single) cross- attention layer then selects these "identity-aware keypoints" to predict root-joint-relative pose parameters of both hands, plus additional parameters such as the translation between the hands and hand shape parameters. We detail below the ...
Then, to facilitate the localization of dense keypoints, we propose a 2D hierarchical binary coding to represent a 2D image position. Specifically, we superpose a grid on the input image and predict which cells contain the desired keypoints. The precision o...
When the relative gripping posture of the object is the same, the distance required to move to the target point is the same; thus, the use of tracking the position change of the keypoints of the wrist can make up for the lack of motion data. For keypoint training data, we collected ...
This value depends on the sequence position of each input and provides the model with a notion of order that it can learn. The original transformer architecture uses a fixed positional encoding, PEPE, which provides a real scalar per-input embedding dimension at position j up to a predefined ...
These Top-Down methods hold a significant position in motion keypoint detection for multimodal robots. They provide robust support for robots in complex tasks and demonstrate excellent performance in small target detection and complex scenes. 2.2. Based on the Bottom-Up Pose Estimation Method Research...