Hybrid Animation: Implementation of Motion Capture

Asraf, Syed Muhammad Hazry; Abdullasim, Nazreen; Romli, Rusnida

doi:10.1088/1757-899x/767/1/012065

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Sagittal plane camera views are more accurate than frontal plane views. Syed Muhammad Hazry Asraf et al [27] proposed the Hybrid animation for motion capture. The transmission of information is a challenge in terms of accuracy and interactivity, making it more appealing and easy for consumers while increasing their exposure.…”

Section: Related Studymentioning

confidence: 99%

Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling

Fangming Dai,

2024

EAI Endorsed Trans Perv Health Tech

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Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.

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Section: Related Studymentioning

confidence: 99%

Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling

Fangming Dai,

2024

EAI Endorsed Trans Perv Health Tech

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ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Motion Model Applicable to VR/AR Devices

Wereszczyński

Michalczuk

Foszner

et al. 2021

Computational Science – ICCS 2021

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Gap Filling Algorithm for Motion Capture Data to Create Realistic Vehicle Animation

WACH,

CHWALEBA

2024

Appl. Comput. Sci.

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The dynamic development of the entertainment market entails the need to develop new methods enabling the application of current scientific achievements. Motion capture is one of the cutting-edge technologies that plays a key role in movement and trajectory computer mapping. The use of optical systems allows one to obtain highly precise motion data that is often applied in computer animations. This study aimed to define the research methodology proposed to analyze the movement of remotely controlled cars utilizing developed gap filling algorithm, a part of post-processing, for creating realistic vehicle animation. On a specially prepared model, six various types of movements were recorded, such as: driving straight line forward, driving straight line backwards, driving on a curve to the left, driving on a curve to the right and driving around a roundabout on both sides. These movements were recorded using a VICON passive motion capture system. As a result, three-dimensional models of vehicles were created that were further post-processed, mainly by filling in the gaps in the trajectories. The case study highlighted problems such as missing points at the beginning and end of the recordings. Therefore, algorithm was developed to solve the above-mentioned problem and allowed for obtaining an accurate movement trajectory throughout the entire route. Realistic animations were created from the prepared data. The preliminary studies allowed one for the verification of the research method and implemented algorithm for obtaining animations reflecting accurate movements.

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Hybrid Animation: Implementation of Motion Capture

Cited by 3 publications

References 3 publications

Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling

Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling

ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Motion Model Applicable to VR/AR Devices

Gap Filling Algorithm for Motion Capture Data to Create Realistic Vehicle Animation

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