MeInGame: Create a Game Character Face from a Single Portrait

TL;DR

This paper introduces an automatic method for creating detailed 3D game character faces from a single portrait, addressing limitations of existing approaches in shape fidelity, texture quality, and integration with game meshes.

Contribution

It proposes a novel pipeline combining low-cost texture acquisition, shape transfer, and training strategies for improved 3D face reconstruction in games.

Findings

Outperforms state-of-the-art game face reconstruction methods

Produces detailed, vivid game characters from single images

Eliminates lighting and occlusion effects in reconstructions

Abstract

Many deep learning based 3D face reconstruction methods have been proposed recently, however, few of them have applications in games. Current game character customization systems either require players to manually adjust considerable face attributes to obtain the desired face, or have limited freedom of facial shape and texture. In this paper, we propose an automatic character face creation method that predicts both facial shape and texture from a single portrait, and it can be integrated into most existing 3D games. Although 3D Morphable Face Model (3DMM) based methods can restore accurate 3D faces from single images, the topology of 3DMM mesh is different from the meshes used in most games. To acquire fidelity texture, existing methods require a large amount of face texture data for training, while building such datasets is time-consuming and laborious. Besides, such a dataset collected under laboratory conditions may not generalized well to in-the-wild situations. To tackle these problems, we propose 1) a low-cost facial texture acquisition method, 2) a shape transfer algorithm that can transform the shape of a 3DMM mesh to games, and 3) a new pipeline for training 3D game face reconstruction networks. The proposed method not only can produce detailed and vivid game characters similar to the input portrait, but can also eliminate the influence of lighting and occlusions. Experiments show that our method outperforms state-of-the-art methods used in games.