CASIA OpenIR  > 多模态人工智能系统全国重点实验室  > 三维可视计算
HIDE: Hierarchical iterative decoding enhancement for multi-view 3D human parameter regression
Lin WT(林伟涛)1,2; Zhang JG(张吉光)1,2; Meng WL(孟维亮)1,2; Liu XL(刘湘龙)2; Zhang XP(张晓鹏)1,2
Source PublicationComputer Animation and Virtual Worlds
2024
Issue35Pages:3
Abstract

Parametric human modeling are limited to either single-view frameworks or simple multi-view frameworks, failing to fully leverage the advantages of easily trainable single-view networks and the occlusion-resistant capabilities of multi-view images. The prevalent presence of object occlusion and self-occlusion in real-world scenarios leads to issues of robustness and accuracy in predicting human body parameters. Additionally, many methods overlook the spatial connectivity of human joints in the global estimation of model pose parameters, resulting in cumulative errors in continuous joint parameters.To address these challenges, we propose a flexible and efficient iterative decoding strategy. By extending from single-view images to multi-view video inputs, we achieve local-to-global optimization. We utilize attention mechanisms to capture the rotational dependencies between any node in the human body and all its ancestor nodes, thereby enhancing pose decoding capability. We employ a parameter-level iterative fusion of multi-view image data to achieve flexible integration of global pose information, rapidly obtaining appropriate projection features from different viewpoints, ultimately resulting in precise parameter estimation. Through experiments, we validate the effectiveness of the HIDE method on the Human3.6M and 3DPW datasets, demonstrating significantly improved visualization results compared to previous methods.

Indexed BySCI
Language英语
IS Representative Paper
Sub direction classification计算机图形学与虚拟现实
planning direction of the national heavy laboratory环境多维感知
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57341
Collection多模态人工智能系统全国重点实验室_三维可视计算
Corresponding AuthorLin WT(林伟涛); Meng WL(孟维亮)
Affiliation1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
2.State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lin WT,Zhang JG,Meng WL,et al. HIDE: Hierarchical iterative decoding enhancement for multi-view 3D human parameter regression[J]. Computer Animation and Virtual Worlds,2024(35):3.
APA Lin WT,Zhang JG,Meng WL,Liu XL,&Zhang XP.(2024).HIDE: Hierarchical iterative decoding enhancement for multi-view 3D human parameter regression.Computer Animation and Virtual Worlds(35),3.
MLA Lin WT,et al."HIDE: Hierarchical iterative decoding enhancement for multi-view 3D human parameter regression".Computer Animation and Virtual Worlds .35(2024):3.
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