Addressing Failure Cases to Enhance Human Motion Conversion Method Performance

Therefore, addressing these failure cases becomes crucial in order to enhance the performance of our method. In the subsequent steps, we will undertake the following measures to overcome these challenges and enhance the quality of generation:

(1) We will develop a more accurate human pose estimation algorithm. It is worth noting that pose estimation technology plays a pivotal role in the conversion of human motion.

(2) Additionally, we will enhance the network model structure and algorithm. Specifically, when a generated limb is missing, the network will generate a limb that aligns with the character, thereby ensuring a more coherent output.

(3) Furthermore, we will improve the data preprocessing stage. This will involve ensuring that the source and target images provided as inputs to the network do not include any third-party elements. By doing so, we can enable the network to concentrate solely on completing the action conversion between the source and target characters.