###### Abstract
The biggest characteristic of \"feedforward\" neural networks is that neurons at the same level cannot provide feedback to each other. Therefore, the entire feedforward neural network can be expressed as a directed acyclic graph without a feedback path. Feedforward constraints facilitate implementation, optimization, and learning. Therefore, feedforward neural networks are among the most mature and widely used neural networks [24; 25; 26; 27]. However, when processing image data, traditional feedforward neural networks require a large number of neurons to read the pixel information of the input image, making them unsuitable for processing high-pixel images. The posture dataset enables the detection of targets, postures, semantic segmentation, and strength segmentation. Recently, several posture datasets have been used for experimental comparisons. The Microsoft COCO 2014 dataset was constructed by [PERSON] et al. (2014) [28] and is a set of 2D image datasets provided by Microsoft. It is widely used for tasks such as object detection, pose detection, semantic segmentation, and strength segmentation. It contains keypoint information about the human body in various scenarios. For the COCO pose detection dataset, there were 120,000 sample images, including approximately 64,000 sample images of the human body. For these images, the positions of each keypoint of the human body (2D coordinates) and the visible and invisible (represented by 0, 1, and 2 for unlabeled, invisible, and visible situations, respectively) were manually annotated. The CMU Panoptic dataset is also used as a typical case study [28]. [PERSON] et al. (2015) [29] constructed a spherical dome area where 480 VGA cameras were placed, each with a resolution of 640 \(\times\) 480 pixels, and they were synchronized using a hardware clock. The captured frame rate was 25 fps. Simultaneously, 31 high-definition cameras were installed to record images at a resolution of 1920 \(\times\) 1080 pixels, operating at 30 fps while ensuring time synchronization with the VGA cameras. Ten Kinect v2 cameras were used to capture the 3D poses of the human body. The Kinect V2 camera has a resolution of 1920 \(\times\) 1080 pixels (RGB) and a depth of 512 \(\times\) 424 pixels (depth). The Kinectv2 camera was operated at 30 fps, and they ensured that the time was the same for all the three types of cameras. The source of JTA data collection was Rockstar's Grand Theft Auto 5 [29]. It adopts a physics-based rendering architecture that produces high-precision character animations. It contains a large number of characters with different postures and costumesfor use, as well as a large number of scenes, and provides various weather conditions. Human3.6M [30] is currently recognized as the largest 3D human posture dataset, which includes 15 daily life actions, such as walking, eating, sitting, and walking a dog. The actions were performed by 11 professional actors wearing motion capture equipment in a laboratory environment and were recorded using four synchronous cameras at 50 Hz, which generated approximately 3.6 million images. Data from the seven actors included 3D annotations. Therefore, this study proposes a series of technical solutions for animation posture recognition and outputs based on neural network structures that exhibit better performance than traditional methods for animation posture output. To overcome the above shortcomings in human posture recognition, this paper proposes the following three improvements in object recognition, 3D posture estimation, and animation generation. (1) Using the concept of \"convolution\" in signals and systems, a CNN is proposed. CNNs can efficiently process large images by allowing their own neurons to output only surrounding cells within the coverage range. A CNN generally consists of two parts; the first half is composed of multiple convolutional layers, and the second half is composed of fully connected neurons.