Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art

Alemi, Omid; Pasquier, Philippe

doi:10.48550/arxiv.1903.08356

Cited by 2 publications

(2 citation statements)

References 67 publications

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“…Wang et al [9] analyzed state-of-the-art techniques in 3D human motion synthesis, with a focus on methods based on motion capture data. Recently, Alemi and Pasquier [10] investigated data-driven movements using machine learning. While their paper includes a review of some deep learning-based methods, there have been numerous advancements in recent years.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Hand Pose Generation Using a Haptic Controller

Choi,

Lee,

et al. 2024

Electronics

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In this study, we present a novel approach to derive hand poses from data input via a haptic controller, leveraging machine learning techniques. The input values received from the haptic controller correspond to the movement of five fingers, each assigned a value between 0.0 and 1.0 based on the applied pressure. The wide array of possible finger movements requires a substantial amount of motion capture data, making manual data integration difficult. This challenge is primary due to the need to process and incorporate large volumes of diverse movement information. To tackle this challenge, our proposed method automates the process by utilizing machine learning algorithms to convert haptic controller inputs into hand poses. This involves training a machine learning model using supervised learning, where hand poses are matched with their corresponding input values, and subsequently utilizing this trained model to generate hand poses in response to user input. In our experiments, we assessed the accuracy of the generated hand poses by analyzing the angles and positions of finger joints. As the quantity of training data increased, the margin of error decreased, resulting in generated poses that closely emulated real-world hand movements.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Hand Pose Generation Using a Haptic Controller

Choi,

Lee,

et al. 2024

Electronics

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“…In contrast to physics-based approaches, data-driven methods produce more realistic and expressive movements, since they use real (prerecorded) movements for training. Nevertheless, they are prone to motion artifacts, such as lack of balance, and still rely on samples "seen" in the training dataset, which renders them incapable of modeling a wide variety of movements [18]. On the contrary, advanced ML techniques and generative modeling approaches are able to synthesize new motion patterns by taking into consideration environmental and time parameters and create movements that are not explicitly defined in their training dataset [18].…”

Section: Goal Of Motion Synthesismentioning

confidence: 99%

Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey

Loi,

Zacharaki,

Moustakas

2024

IEEE Trans. Visual. Comput. Graphics

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The inference of 3D motion and dynamics of the human musculoskeletal system has traditionally been solved using physics-based methods that exploit physical parameters to provide realistic simulations. Yet, such methods suffer from computational complexity and reduced stability, hindering their use in computer graphics applications that require real-time performance. With the recent explosion of data capture (mocap, video) machine learning (ML) has started to become popular as it is able to create surrogate models harnessing the huge amount of data stemming from various sources, minimizing computational time (instead of resource usage), and most importantly, approximate real-time solutions. The main purpose of this paper is to provide a review and classification of the most recent works regarding motion prediction, motion synthesis as well as musculoskeletal dynamics estimation problems using ML techniques, in order to offer sufficient insight into the state-of-the-art and draw new research directions. While the study of motion may appear distinct to musculoskeletal dynamics, these application domains provide jointly the link for more natural computer graphics character animation, since ML-based musculoskeletal dynamics estimation enables modeling of more long-term, temporally evolving, ergonomic effects, while offering automated and fast solutions. Overall, our review offers an in-depth presentation and classification of ML applications in human motion analysis, unlike previous survey articles focusing on specific aspects of motion prediction.

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Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art

Cited by 2 publications

References 67 publications

Machine Learning-Based Hand Pose Generation Using a Haptic Controller

Machine Learning-Based Hand Pose Generation Using a Haptic Controller

Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey

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