Real‐time 3D human pose and motion reconstruction from monocular RGB videos

Yiannakides, Anastasios; Aristidou, Andreas; Chrysanthou, Yiorgos

doi:10.1002/cav.1887

Cited by 15 publications

(10 citation statements)

References 54 publications

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“…Then it is possible to transform the video data to 3D motion data. While the data qualities are not satisfactory, estimated from 2D videos, 39‐41 constructing 3D motion database from 2D videos is promising. Second, we do not fully utilize the interpretability of the LMA features though the hybrid feature is beneficial for improving the emotional classification.…”

Section: Resultsmentioning

confidence: 99%

EmoDescriptor: A hybrid feature for emotional classification in dance movements

Bai

Dai

et al. 2021

Computer Animation & Virtual

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Similar to language and music, dance performances provide an effective way to express human emotions. With the abundance of the motion capture data, content‐based motion retrieval and classification have been fiercely investigated. Although researchers attempt to interpret body language in terms of human emotions, the progress is limited by the scarce 3D motion database annotated with emotion labels. This article proposes a hybrid feature for emotional classification in dance performances. The hybrid feature is composed of an explicit feature and a deep feature. The explicit feature is calculated based on the Laban movement analysis, which considers the body, effort, shape, and space properties. The deep feature is obtained from latent representation through a 1D convolutional autoencoder. Eventually, we present an elaborate feature fusion network to attain the hybrid feature that is almost linearly separable. The abundant experiments demonstrate that our hybrid feature is superior to the separate features for the emotional classification in dance performances.

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Section: Resultsmentioning

confidence: 99%

EmoDescriptor: A hybrid feature for emotional classification in dance movements

Bai

Dai

et al. 2021

Computer Animation & Virtual

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“…These have been inspired by various classifications of prediction methods: 1) 2D Motion trajectory prediction: it mainly predicts motion trajectories for human or moving devices [42,43,97,100,101,84,85] in a 2D plane. 2) Video prediction: it focuses on the motion prediction on the video frames [109,90,69,105]. 3) Poses sequence prediction: it predicts future 3D human motions.…”

Section: Scopementioning

confidence: 99%

3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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“…Hereid et al 29 developed 3D dynamic walking patterns for under-actuated humanoid models and discussed regarding the hybrid zero dynamics. Yiannakides et al 30 used data obtained from monocular RGB videos for 3D human pose and motion reconstruction in real-time. Wehner and Bennewitz 31 developed an approach to optimize the stable gait pattern of a humanoid robot in order to provide it a human like motion.…”

Section: Introductionmentioning

confidence: 99%

Humanoid navigation: A firefly based approach

Kumar

Parhi

Sethy

2021

Computer Animation & Virtual

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Humanoid navigation has been the center of attraction among robotic researchers since many years by virtue of their increasing use in industrial automation and smart manufacturing. In the current research, a firefly based computer vision integrated navigational analysis has been performed on humanoid robots for smooth movement by negotiation with obstacles present in complicated terrains. Here, the logic of the firefly algorithm has been used to design the controller by careful consideration of the navigational parameters. A computer vision based method is integrated along with the developed controller to resolve some conflicting situations that may arise by encountering large sized obstacle or detection of an obstacle exactly in front of the robot where the robot becomes confused regarding the direction of turn. The developed navigational model has been tested in simulation environments by considering NAO robot as the humanoid platform. The simulation results are also verified through an experimental platform developed under research laboratory conditions. The data obtained from both the platforms are compared in terms of navigational parameters and are found to be in close proximity to each other.Finally, the developed navigational model is also authenticated against other existing models for validating better efficiency.

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Real‐time 3D human pose and motion reconstruction from monocular RGB videos

Cited by 15 publications

References 54 publications

EmoDescriptor: A hybrid feature for emotional classification in dance movements

EmoDescriptor: A hybrid feature for emotional classification in dance movements

3D Human Motion Prediction: A Survey

Humanoid navigation: A firefly based approach

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