通过生成模型生成多样化的穿衣三维人体动画

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Min Shi, Wenke Feng, Lin Gao, Dengming Zhu
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引用次数: 0

摘要

数据驱动的服装动画是计算机制图行业当前关注的话题。现有方法通常在单一人体姿势或时间姿势序列与服装变形之间建立映射关系,但很难快速生成多样化的人体着装动画。为了解决这个问题,我们采用了一种方法,根据指定的人体运动标签自动合成具有时间一致性的着装人体动画。我们方法的核心是一个两阶段策略。具体来说,我们首先利用基于变换器的条件变异自动编码器(Transformer-CVAE)学习一个潜在空间,对人体动作的序列级分布进行编码。然后,服装模拟器利用变压器编码器-解码器架构合成动态服装形状。由于学习到的潜在空间来自不同的人体动作,因此我们的方法可以在特定动作标签下生成各种风格的动作。通过新颖的序列开始(BOS)学习策略和自我监督完善程序,我们的服装模拟器能够高效地合成与生成的人体动作相对应的服装变形序列,同时保持时间和空间的一致性。我们通过实验验证了我们的想法。这是第一个直接为人体动画穿衣的生成模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Generating diverse clothed 3D human animations via a generative model

Generating diverse clothed 3D human animations via a generative model

Data-driven garment animation is a current topic of interest in the computer graphics industry. Existing approaches generally establish the mapping between a single human pose or a temporal pose sequence, and garment deformation, but it is difficult to quickly generate diverse clothed human animations. We address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion label. At the heart of our method is a two-stage strategy. Specifically, we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder (Transformer-CVAE). Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder–decoder architecture. Since the learned latent space comes from varied human motions, our method can generate a variety of styles of motions given a specific motion label. By means of a novel beginning of sequence (BOS) learning strategy and a self-supervised refinement procedure, our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial consistency. We verify our ideas experimentally. This is the first generative model that directly dresses human animation.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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