Game Character Modeling And Reconstruction Method

Many new technologies such as virtual reality,augmented reality,and deep learning are coming to the dividend period due to the explosion of data transmission due to the arrival of the 5G era.These technologies are the most widely used in the game entertainment industry.Game character modeling is important and arduous,and is an important part of the industry.However,there are many problems with various character reconstruction methods: a.Mainstream technologies obtain 3D character data through special 3D scanning instruments or obtain 3D character models under green screen conditions by wearing special clothing.These methods are expensive and complicated;b.The market method uses art and animation software to generate the model,the labor cost is too high,and the efficiency is too low;c.The academic method uses neural network algorithm to realize the character reconstruction.Due to the low cost,high efficiency,and high degree of automation,it begins to replace the previous two methods.However,due to the lack of statistical model tools that contain comprehensive information about the characters,the final visual effect of the model is not ideal.In order to cope with the current game character modeling problems,based on the character statistical model MANO and the head statistical model FLAME,on the basis of thorough research on these two models,the author summarizes the model learning strategy with reference to the construction process of the model,using the same The learning strategy proposes a new character statistical model MAFA and combines the existing frontier neural network algorithms Alphapose and OPENPOSE to obtain two methods that can fully capture character features and quickly model.The main work of this article includes:1.Build a human body shape data set from the open source human body shape scan file,which is used to train the human body shape model MAFA for body shape parameter training.This data set can be used as a standard human body shape data set for other model training;2.Established MAFA,a statistical model of human figures,including figure,posture,gestures and facial expressions,and described in detail the formula meaning of the model,data set construction and processing flow,body parameter training process,skin weight refresh process and Migration criteria for other parameters;3.Based on the character statistical model MAFA and the leading character bone feature information extraction algorithms OPENPOSE and Alphapose,two rapid character modeling schemes are proposed.Through the establishment of comparative experiments,the effect of MANO and MAFA on fitting 3D bone information of characters is further verified.MAFA engineering capabilities are further verified.And improve the Alphapose algorithm to realize the rapid conversion of 2D video stream to 3D animation FBX file.Compared with other character reconstruction algorithms,this article features the character statistical model MAFA,which has the following advantages:(1)It can be well applied to 3D game engines such as Maya;(2)It has wide application prospects in the neighborhood of image graphics It will become a crucial experimental tool in many deep learning algorithms;(3)The model module is perfect,including the figure,posture,gestures and facial expressions of the characters.