Human Pose Regression by Combining Indirect Part Detection and Contextual Information

Luvizon, Diogo C.; Tabia, Hedi; Picard, David

doi:10.48550/arxiv.1710.02322

Cited by 8 publications

(10 citation statements)

References 0 publications

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“…The related works [29,34] only experimented with 2D pose estimation on MPII benchmark [2]. They also have limited ablation experiments.…”

Section: Methodology For Comprehensive Experimentsmentioning

confidence: 99%

“…They also have limited ablation experiments. Specifically, [29] provides only system-level comparison results without any ablation experiments. [34] studies the heat map normalization methods, heat map regularization and backbone networks, which is far less comprehensive than ours.…”

Section: Methodology For Comprehensive Experimentsmentioning

confidence: 99%

“…It has been known as soft-argmax and used in the previous works [27,52,45]. Specifically, two contemporary works [29,34] also apply it for human pose estimation. Nevertheless, these works have limited ablation experiments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integral Human Pose Regression

Sun

Xiao

Wei

et al. 2018

Computer Vision – ECCV 2018

624

623

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State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as non-differentiable postprocessing and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time.

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“…The related works [29,34] only experimented with 2D pose estimation on MPII benchmark [2]. They also have limited ablation experiments.…”

Section: Methodology For Comprehensive Experimentsmentioning

confidence: 99%

Section: Methodology For Comprehensive Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Integral Human Pose Regression

Sun

Xiao

Wei

et al. 2018

Computer Vision – ECCV 2018

624

623

View full text Add to dashboard Cite

show abstract

“…It's not difficult to understand that there exists an intrinsic connection between human actions and human poses (Wei et al 2016;Liu et al 2018;Cao et al 2017;Ning et al 2017;Luvizon et al 2017;Xiaohan Nie et al 2015). Different people may have different skin colors and appearance, dressing various clothes, but their poses are similar when they are doing the same action due to the homogeneity of human body.…”

Section: Combination Of Action Recognition and Pose Estimationmentioning

confidence: 99%

Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

Feng

Liu

et al. 2019

AAAI

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Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects' localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a closed loop to utilize the complementary information iteratively, which can be trained in an end-to-end manner. The proposed method achieves the state-of-the-art performance on two public benchmarks including Verbs in COCO (V-COCO) and HICO-DET datasets.

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“…The intermediate supervision at each hourglass module benefits from previous module outputs, refining and improving final network predictions. Given its high performance, its conceptual simplicity, and that allows for an easy multitask integration among stacked modules, this architecture is serving as a baseline model in several works [30], [31], [32], [33], [34].…”

Section: A Multi-task Architecturementioning

confidence: 99%

Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation

Sánchez

Oliu

Madadi

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four related human analysis tasks in still images in a multi-task scenario by leveraging such datasets. Specifically, we study the correlation of 2D/3D pose estimation, body part segmentation and full-body depth estimation. These tasks are learned via the well-known Stacked Hourglass module such that each of the task-specific streams shares information with the others. The main goal is to analyze how training together these four related tasks can benefit each individual task for a better generalization. Results on the newly released SURREAL dataset show that all four tasks benefit from the multi-task approach, but with different combinations of tasks: while combining all four tasks improves 2D pose estimation the most, 2D pose improves neither 3D pose nor full-body depth estimation. On the other hand 2D parts segmentation can benefit from 2D pose but not from 3D pose. In all cases, as expected, the maximum improvement is achieved on those human body parts that show more variability in terms of spatial distribution, appearance and shape, e.g. wrists and ankles.

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Human Pose Regression by Combining Indirect Part Detection and Contextual Information

Cited by 8 publications

References 0 publications

Integral Human Pose Regression

Integral Human Pose Regression

Turbo Learning Framework for Human-Object Interactions Recognition and Human Pose Estimation

Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation

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