MetaFi++: WiFi-Enabled Transformer-Based Human Pose Estimation for Metaverse Avatar Simulation

Zhou, Yong‐Wu; Huang, He; Yuan, Shenghai; Zou, Han; Xie, Lihua; Wang, Dazhuo

doi:10.1109/jiot.2023.3262940

Cited by 33 publications

(2 citation statements)

References 41 publications

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“…The goal of monocular 3D human pose estimation (HPE) is to localize 3D body joints from 2D images or video. This task is crucial in computer vision applications, such as human-robot interaction [1], autonomous driving [2], the metaverse [3], and VR [4]. In recent years, coupled with the success of deep learning, the performance of monocular 3D HPE has made notable progress [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

HDPose: Post-Hierarchical Diffusion with Conditioning for 3D Human Pose Estimation

Lee,

Kim

2024

Sensors

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Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous studies have proposed diffusion model-based approaches (DDPM) that learn to reconstruct a correct 3D pose from a noisy initial 3D pose. In addition, these approaches use 2D keypoints or context encoders that encode spatial and temporal information to inform the model. However, they often fall short of achieving peak performance, or require an extended period to converge to the target pose. In this paper, we proposed HDPose, which can converge rapidly and predict 3D poses accurately. Our approach aggregated spatial and temporal information from the condition into a denoising model in a hierarchical structure. We observed that the post-hierarchical structure achieved the best performance among various condition structures. Further, we evaluated our model on the widely used Human3.6M and MPI-INF-3DHP datasets. The proposed model demonstrated competitive performance with state-of-the-art models, achieving high accuracy with faster convergence while being considerably more lightweight.

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Section: Introductionmentioning

confidence: 99%

HDPose: Post-Hierarchical Diffusion with Conditioning for 3D Human Pose Estimation

Lee,

Kim

2024

Sensors

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“…The Metaverse is a novel and promising technology in the digital realm that has the potential to transform healthcare, science, technology, entertainment, economy, and training by providing realistic experiences for people and professionals [1]. It combines various cutting-edge technologies, including artificial intelligence (AI), virtual reality (VR) [2] and augmented reality (AR) [3], Internet of Things (IoT) [4,5], robotics, blockchain [6], and quantum computing [7]. This convergence of technologies opens new avenues for delivering high-quality services in all aspects of our lives.…”

Section: Introductionmentioning

confidence: 99%

The Meta-Metaverse: Ideation and Future Directions

Jamshidi,

Dehghaniyan Serej,

Jamshidi

et al. 2023

Future Internet

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In the era of digitalization and artificial intelligence (AI), the utilization of Metaverse technology has become increasingly crucial. As the world becomes more digitized, there is a pressing need to effectively transfer real-world assets into the digital realm and establish meaningful relationships between them. However, existing approaches have shown significant limitations in achieving this goal comprehensively. To address this, this research introduces an innovative methodology called the Meta-Metaverse, which aims to enhance the immersive experience and create realistic digital twins across various domains such as biology, genetics, economy, medicine, environment, gaming, digital twins, Internet of Things, artificial intelligence, machine learning, psychology, supply chain, social networking, smart manufacturing, and politics. The multi-layered structure of Metaverse platforms and digital twins allows for greater flexibility and scalability, offering valuable insights into the potential impact of advancing science, technology, and the internet. This article presents a detailed description of the proposed methodology and its applications, highlighting its potential to transform scientific research and inspire groundbreaking ideas in science, medicine, and technology.

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