HUMBI: A Large Multiview Dataset of Human Body Expressions

Yu, Zhixuan; Yoon, Jae Shin; Lee, In Kyu; Venkatesh, Prashanth; Park, Jaesik; Yu, Jihun; Park, Hyun Soo

doi:10.1109/cvpr42600.2020.00306

Cited by 75 publications

(47 citation statements)

References 63 publications

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“…A person's emotional state is often conveyed through bodily expression. As such, analyzing body based activities, including action, gesture and posture are the popular research topics [75,36,23,62,67,71,24,53,93] in the community. However, these datasets focused on recognizing human activities (e.g., a man is jumping), rarely related to the emotional states.…”

Section: Related Workmentioning

confidence: 99%

iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

Liu

Shi

Chen

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE). Different from existing public datasets, iMiGUE focuses on nonverbal body gestures without using any identity information, while the predominant researches of emotion analysis concern sensitive biometric data, like face and speech. Most importantly, iMiGUE focuses on micro-gestures, i.e., unintentional behaviors driven by inner feelings, which are different from ordinary scope of gestures from other gesture datasets which are mostly intentionally performed for illustrative purposes. Furthermore, iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly). This is because the real need for emotion AI is to understand the emotional states behind gestures in a holistic way. Moreover, to counter for the challenge of imbalanced sample distribution of this dataset, an unsupervised learning method is proposed to capture latent representations from the micro-gesture sequences themselves. We systematically investigate representative methods on this dataset, and comprehensive experimental results reveal several interesting insights from the iMiGUE, e.g., micro-gesture-based analysis can promote emotion understanding. We confirm that the new iMiGUE dataset could advance studies of micro-gesture and emotion AI.

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Section: Related Workmentioning

confidence: 99%

iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

Liu

Shi

Chen

et al. 2021

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Datasets for 3D human motion and interactions. A large number of datasets focus on 3D human pose and motion from third-person views [16,23,27,30,31,43,52,57,71,74,75,84,90]. For example, Human3.6M [27] and AMASS [51] use optical marker-based motion capture to collect large amounts of high-quality 3D motion sequences; they are limited to constrained studio setups and images -when available -are polluted by marker data.…”

Section: Related Workmentioning

confidence: 99%

“…You2Me [55] similarly focuses on egocentric body pose prediction, annotating 3D ground-truth skeletons from image sequences captured with a chest-mounted camera plus external cameras. EgoMoCap [47] analyzes the second- [84] 380k 772 * TotalCapture [71] 1,900k 5 Human3.6M [27] 3,600k 11 Mo2Cap2 [80] 15k 5 You2Me [55] 150k 10 HPS [22] 300k 7…”

Section: Related Workmentioning

confidence: 99%

EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices

Zhang¹,

Qian²,

Zhang³

et al. 2021

Preprint

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“…Alternatively, several methods use marker-less motion capture, e.g. MuPoTS-3D [37], PanopticStudio [26], MPI-INF-3DHP-Test [36], and HUMBI [56]. Such methods are typically less accurate than marker-based systems, but they avoid intrusive markers, allow more varied clothing, and sometimes are used in more realistic scenes e.g.…”

Section: Related Workmentioning

confidence: 99%

AGORA: Avatars in Geography Optimized for Regression Analysis

Patel¹,

Huang²,

Tesch³

et al. 2021

Preprint

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includes 257 scans of children. We create reference 3D poses and body shapes by fitting the SMPL-X body model (with face and hands) to the 3D scans, taking into account clothing. We create around 14K training and 3K test images by rendering between 5 and 15 people per image using either image-based lighting or rendered 3D environments, taking care to make the images physically plausible and photoreal. In total, AGORA consists of 173K individual person crops. We evaluate existing state-of-theart methods for 3D human pose estimation on this dataset. and find that most methods perform poorly on images of children. Hence, we extend the SMPL-X model to better capture the shape of children. Additionally, we finetune methods on AGORA and show improved performance on both AGORA and 3DPW, confirming the realism of the dataset. We provide all the registered 3D reference training data, rendered images, and a web-based evaluation site at https://agora.is.tue.mpg.de/.

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HUMBI: A Large Multiview Dataset of Human Body Expressions

Cited by 75 publications

References 63 publications

iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis

EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices

AGORA: Avatars in Geography Optimized for Regression Analysis

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