多模态信息引导的三维数字人运动生成综述

赵宝全; 付一愉; 苏卓; 王若梅; 吕辰雷; 罗笑南

doi:10.11834/jig.230626

数字人建模、生成与渲染技术 | 浏览量 : 0 下载量: 3830 CSCD: 0

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多模态信息引导的三维数字人运动生成综述
A survey on multimodal information-guided 3D human motion generation
2024年29卷第9期页码：2541-2565
收稿：2023-09-05，

修回：2024-01-26，

纸质出版：2024-09-16
DOI： 10.11834/jig.230626
稿件说明：

移动端阅览

赵宝全，付一愉，苏卓，王若梅，吕辰雷，罗笑南. 2024. 多模态信息引导的三维数字人运动生成综述. 中国图象图形学报， 29(09):2541-2565 DOI： 10.11834/jig.230626.

Zhao Baoquan， Fu Yiyu， Su Zhuo， Wang Ruomei， Lyu Chenlei， Luo Xiaonan. 2024. A survey on multimodal information-guided 3D human motion generation. Journal of Image and Graphics， 29(09):2541-2565 DOI： 10.11834/jig.230626.

摘要

基于多模态信息的三维数字人运动生成技术旨在通过文本、音频、图像和视频等数据实现特定输入条件下的人体运动生成。这项技术在电影、动画、游戏制作和元宇宙等领域具有重要的应用价值和广泛的经济社会效益，是近年来计算机图形学和计算机视觉等领域研究的热点问题之一。然而，基于多模态信息的三维数字人运动生成面临着诸多挑战，包括跨模态信息的表征和融合困难、高质量数据集缺乏、生成的运动质量较差（如抖动、穿模和脚部滑动等）以及生成效率低等问题。虽然近年来研究者们提出了各式各样的解决方案来应对上述挑战，但如何根据不同模态数据的特点实现高效、高质量的三维数字人运动生成仍然是一个开放性问题。本文以数字人运动生成所采用的模型架构为分类标准，将现有的主流方法分为基于生成对抗网络（generative adversarial network，GAN）的方法、基于自编码器（autoencoder，AE）的方法、基于变分自编码器（variational autoencoder，VAE）的方法以及基于扩散模型的方法，总结并形成了一种数字人运动生成通用框架。本文还介绍了该领域常见的参数化人体模型、数据集以及评估指标。对于一些具有代表性的工作，本文在一些常用数据集上进行了对比实验，评估这些方法的性能表现。最后综合现有的数据集、算法和代表性研究，总结了该领域的问题和挑战，探讨了完善数据集、优化运动质量和多样性、融合跨模态信息和提高生成效率等潜在的研究方向。

Abstract

Three-dimensional （3D） digital human motion generation guided by multimodal information generates human motion under specific input conditions through data， such as text， audio， image， and video. This technology has a wide spectrum of applications and extensive economic and social benefits in the fields of film， animation， game production， metaverse， etc.， and is one of the research hotspots in the fields of computer graphics and computer vision. However， such a task faces grand challenges， including the difficult representation and fusion of multimodal information， lack of high-quality datasets， poor quality of generated motion （such as jitter， penetration， and foot sliding）， and low generation efficiency. Although various solutions have been proposed to address the aforementioned challenges， a mechanism for achieving efficient and high-quality 3D digital human motion generation based on the characteristics of distinct modal data remains an open problem to be solved. This paper comprehensively reviews 3D digital human motion generation and elaborates on related recent advances from the perspectives of parametrized 3D human models， human motion representation， motion generation techniques， motion analysis and editing， existing human motion datasets and evaluation metrics. Parametrized human models facilitate digital human modeling and motion generation through the provision of parameters associated with body shapes and postures and serve as key pillars of current digital human research and applications. This survey begins with an introduction to widely used parametrized 3D human body models， including shape completion and animation of people （SCAPE）， skinned multi-person linear model （SMPL）， SMPL-X， and SMPL-H， and their detailed comparison in terms of model representations and the parameters used to control body shapes， poses， and facial expressions. Human motion representation is a core issue in digital human motion generation. This work highlights the musculoskeletal model and classic skinning algorithms， including linear blending skinning and dual quaternion skinning， and their application in physics-based and data-driven methods to control human movements. We have also extensively studied approaches to existing multimodal information-guided human motion generation and categorized them into four major branches， i.e.， generative adversarial network-， autoencoder-， variational autoencoder-， and diffusion model-based methods. Other works， such as generative motion matching， have also been mentioned and compared with data-driven methods. The survey summarizes existing schemes of human motion generation from the perspectives of methods and model architectures and presents a unified framework for the generation of digital human motion. A motion encoder extracts motion features from an original motion sequence and fuses them with the conditional characteristics extracted by the conditional encoder into latent variables or maps them to the latent space. This condition enables generative adversarial networks， autoencoders， variational autoencoders， or diffusion models to generate qualified human movements through a motion decoder. In addition， this paper surveys the current work on digital human motion analysis and editing， including motion clustering， motion prediction， motion in-betweening， and motion in-filling. Data-driven human motion generation and evaluation requires the use of a high-quality dataset. We collected publicly available human motion databases and classified them into various types based on two criteria. From the perspective of data type， existing databases can be classified into motion capture and video reconstruction datasets. Motion capture data sets rely on devices， such as motion capture systems， cameras， and inertial measurement units， to obtain real human movement data （i.e.， ground truth）. Meanwhile， the video reconstruction dataset was used to reconstruct a 3D human body model through estimation of body joints from motion videos and fitting them to a parametric human body model. From the perspective of task type， commonly used databases can be classified into text-， action-， and audio-motion datasets. The new datasets are usually obtained by processing motion capture and video reconstruction datasets based on specific tasks. A comprehensive briefing on the evaluation metrics of 3D human motion generation， including motion quality， motion diversity， and multimodality， consistency between inputs and outputs， and inference efficiency， is also provided. Apart from objective evaluation metrics， user study was employed to generate human motion quality and was discussed in this paper. To compare the performances of various generation methods used in digital human motion on public datasets， we selected a collection of the most representative work and carried out extensive experiments for comprehensive evaluation. Finally， the well-addressed and underexplored issues in this field were summarized， and several potential further research directions regarding datasets， the quality and diversity of generated motions， cross-modal information fusion， and generation efficiency were discussed. Specifically， existing datasets generally fail to meet the expectations concerning motion diversity and descriptions associated with motions， data distribution， and length of motion sequence. Future work should consider the development of a large-scale 3D human motion database to boost the efficacy and robustness of motion generation models. In addition， the quality of generated human motions， especially those with complex movement patterns， remains dissatisfactory. Physical constraints and postprocessing show promise in the integration into human motion generation frameworks to tackle issues. In addition， although human-motion generation methods can generate various motion sequences from multimodal information， such as text， audio， music， actions and keyframes， work on cross-modal human motion generation （e.g.， generating a motion from a text description and a piece of background music） is scarcely reported. Investigation of such a task is worthy， especially in unlocking new opportunities in this area. In terms of the diversity of generated content， some researchers have explored harvesting rich， diverse， and stylized motions using variational autoencoders， diffusion models， and contrastive language-image pretraining neural networks. However， current studies mainly focus on the motion generation of a single human represented by an SMPL-like naked parameterized 3D model. Meanwhile， the generation and interaction of multiple dressed humans have huge untapped application potential but have not received sufficient attention. Finally， another nonnegligible issue is a mechanism for boosting motion generation efficiency and achieving a good balance between quality and inference overhead. Possible solutions to such a problem include lightweight parameterized human models， information-intensive training datasets， and improved or more advanced generative frameworks.

关键词

Keywords

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