3D face creation via 2D images within blender virtual environment

Abstract

<p><span>Animation and virtual reality movie-making technologies are still witnessing significant progress to this day. Building and stimulating virtual characters inside these applications is a goal. Build a 3D face via using some special tools inside the virtual world is the most important part of identifying a 3D animation. Keen Tools Face Builder add-on for Blender. Interested in creating a 3D face of a famous figure, artist or the general public by adopting several 2D images added to the virtual blinder software environment. The main problem facing these tools is that they deal with high-resolution and sharpness pictures because some images that contain blurring, the result is to build a 3D face model that contains design distortions and non- clearly. in this proposed paper, build a data set for 2D pictures of a specific character (actor), at a resolution of 1920 x 1080 pixels. These images were caught by the camera, different in sharpness and blurring (four types of blurry). Using the “Laplacian Filter algorithm” and OpenCV library with Python language, to isolate blurry from sharpness 2D images. Sharpness images used to build a 3D face model that gave real and similar results to the character in the pictures. </span></p>

[Retracted] 3D Film Animation Image Acquisition and Feature Processing Based on the Latest Virtual Reconstruction Technology

“…e detection model based on human variable parts can deal with the problem of occlusion and pose change very well, and the deformable parts model proposed by Xia et al does not require image preprocessing, but by dividing the human model into several pieces and then fusing the results of each part detection to determine the presence of the human body [9]. is has a strong detection performance but requires the construction of many templates to achieve a good matching effect in the case of occlusion and pose change [4]. e most important step of the R-CNN algorithm based on the region suggestion frame proposed by Bladin et al is to perform a selective search algorithm to get the candidate regions, then normalize the candidate domains by convolutional neural network and perform the complex convolutional computation to extract the convolutional features in the image target, and finally use support vector machine to classify the data according to the features [1].…”

“…e combination of virtual reality and human motion capture techniques can be used for the simulation of virtual scenes. In crowd evacuation simulation of buildings, based on the simulation of human motion in obstructed and narrow passages, it helps to design safer and reasonable escape routes, thus reducing casualties and losses [4]. Also, virtual simulation technology is important for the study of ergonomics.…”

PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images

“…Where I (H ) , F (H ), B (H ) are the input image map in the frontal and backside. (X) denotes a 3D human model reconstructed and rendered in a blender virtual environment to display the 3D human reconstruction [51].…”