Name
Abstract | ||
|---|---|---|
ABSTRACTVirtual character has been widely adopted in many areas, such as virtual assistant, virtual customer service, robotics and etc. In this paper, we focus on its application in e-commerce live streaming. Particularly, we propose a virtual character generation and animation system that supports e-commerce live streaming with virtual characters as anchors. The system offers a virtual character face generation tool based on a weakly supervised 3D face reconstruction method. The method takes a single photo as input and generates a 3D face model with both similarity and aesthetics considered. It does not require 3D face annotation data due to the assist of differentiable neural rendering technique which seamlessly integrates rendering into a deep learning based 3D face reconstruction framework. Moreover, the system provides two animation approaches which support two different ways of live stream respectively. The first approach is based on real-time motion capture. An actor's performance is captured in real-time via a monocular camera, and then utilized for animating a virtual anchor. The second approach is text driven animation, in which the human-like animation is automatically generated based on a text script. The relationship between text script and animation is learned based on the training data which can be accumulated via the motion capture based animation. To our best knowledge, the presented work is the first sophisticated virtual character generation and animation system that is designed for e-commerce live streaming and actually deployed on an online shopping platform with millions of daily audiences. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3474085.3481547 | International Multimedia Conference |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Hu | 1 | 0 | 0.68 |
| Bang Zhang | 2 | 0 | 1.01 |
| Peng Zhang | 3 | 0 | 0.68 |
| Jinwei Qi | 4 | 152 | 9.24 |
| Jian Cao | 5 | 0 | 0.34 |
| Daiheng Gao | 6 | 0 | 1.35 |
| Haiming Zhao | 7 | 0 | 0.34 |
| Xiaoduan Feng | 8 | 0 | 0.34 |
| Qi Wang | 9 | 8 | 12.82 |
| Lian Zhuo | 10 | 0 | 0.34 |
| Pan Pan | 11 | 0 | 0.68 |
| Yinghui Xu | 12 | 172 | 20.23 |