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PhysDiff: Physics-Guided Human Motion Diffusion Model [PDF]
IEEE International Conference on Computer Vision, 2022 Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions ...
Ye Yuan +4 more
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Analysis of the Use of Circular Motion Physics Concepts on Game Rides in Jawa Timur Park 1
Studies in Philosophy of Science and Education, 2021 In general, physics has been widely spread well in everyday life, one of which is used as a concept in holiday destinations in Indonesia. In East Java, Jatim Park group has been established by a combination of education and entertainment, resulting in ...
Kirana Aureola Arzak +2 more
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Micromachines, 2015 A quantitative description of the dynamics of droplet motion has been a long-standing concern in electrowetting research. Although many static and dynamic models focusing on droplet motion induced by electrowetting-on-dielectric (EWOD) already exist ...
Weiwei Cui +5 more
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Students’ Ability In Solving Physics Problems on Newtons’ Law of Motion
Jurnal Ilmiah Pendidikan Fisika Al-Biruni, 2018 The ability to solve physics problems is one of the goals in learning physics and a part of the current curriculum demands. One of the physics problems that are often the focus of attention in learning is Newton's law of motion.
Supeno Supeno +2 more
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An example of motion in a course of physics for agriculture [PDF]
The Physics Teacher, 1984 Ivan Amaral Guerrini
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Universal Humanoid Motion Representations for Physics-Based Control [PDF]
International Conference on Learning Representations, 2023 We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods ...
Zhengyi Luo +6 more
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MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations [PDF]
ACM Transactions on Graphics, 2023 In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based reinforcement learning, our ...
Heyuan Yao +5 more
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Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors [PDF]
Computer Vision and Pattern Recognition, 2022 Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum of the camera ...
Xinyu Yi +6 more
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Physics Constrained Motion Prediction with Uncertainty Quantification [PDF]
2023 IEEE Intelligent Vehicles Symposium (IV), 2023 Predicting the motion of dynamic agents is a critical task for guaranteeing the safety of autonomous systems. A particular challenge is that motion prediction algorithms should obey dynamics constraints and quantify prediction uncertainty as a measure of
Renukanandan Tumu +3 more
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