The proposed architecture of the skeletal transformer can be adapted for other related tasks, such as hand pose estimation or object manipulation, by making the following modifications:
Hand Pose Estimation: For hand pose estimation, the skeletal transformer can be modified to focus on the hand joints and their interactions. By adjusting the input data and the decoder architecture to cater specifically to hand poses, the model can be tailored for accurate hand pose estimation in various scenarios.
Object Manipulation: To adapt the skeletal transformer for object manipulation tasks, the model can be trained on datasets that include object interactions and hand-object relationships. By incorporating additional features related to object properties and interactions, the model can learn to predict poses and shapes relevant to object manipulation tasks.
Fine-grained Pose Estimation: For tasks requiring fine-grained pose estimation, such as facial expressions or detailed body movements, the skeletal transformer can be enhanced with higher-resolution input data and finer-grained output representations. This can enable the model to capture subtle movements and nuances in pose estimation.
Multi-Modal Fusion: To handle tasks involving multiple modalities, such as combining visual and depth data for 3D pose estimation, the skeletal transformer can be extended to incorporate multi-modal fusion techniques. By integrating information from different modalities, the model can improve accuracy and robustness in complex scenarios.
Dynamic Environments: For tasks in dynamic environments with changing backgrounds or lighting conditions, the model can be adapted to incorporate dynamic context-aware mechanisms. By considering environmental factors in the pose estimation process, the model can adapt to varying conditions and improve performance in dynamic settings.