Fast Terrain-Adaptive Motion Generation using Deep Neural Networks

Yu, Moonwon; Kwon, Byungjun; Kim, Jongmin; Kang, Shin-Jin; Jang, Hye-Young

doi:10.1145/3355088.3365157

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Transition Generation1

Motion Synthesis1

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2021

2024

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Cited by 2 publications

(2 citation statements)

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“…However, this conversion of motion sequences into images lacks interpretability, and commonly produces artifacts such as jittering and foot sliding. Another work [46] only interpolates the body joint trajectory and generates the corresponding pose based on the interpolated trajectory. It generates animations for hundreds of characters simultaneously.…”

Section: Transition Generationmentioning

confidence: 99%

Diverse Motion In-betweening from Sparse Keyframes with Dual Posture Stitching

Ren,

Yu,

Guo

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Section: Transition Generationmentioning

confidence: 99%

Diverse Motion In-betweening from Sparse Keyframes with Dual Posture Stitching

Ren,

Yu,

Guo

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…Yu et al. [YKK*19] proposed a simple scheme for interpolating a motion sequence from starting and ending positions of end‐effector joints. Their main objective is computational efficiency, in order to generate terrain‐adaptive character motion in real time for video games.…”

Section: Motion Synthesismentioning

confidence: 99%

A Survey on Deep Learning for Skeleton‐Based Human Animation

Mourot

Hoyet

Clerc³

et al. 2021

Computer Graphics Forum

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Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning (DL) and deep reinforcement learning (DRL). In this article, we propose a comprehensive survey on the state‐of‐the‐art approaches based on either DL or DRL in skeleton‐based human character animation. First, we introduce motion data representations, most common human motion datasets and how basic deep models can be enhanced to foster learning of spatial and temporal patterns in motion data. Second, we cover state‐of‐the‐art approaches divided into three large families of applications in human animation pipelines: motion synthesis, character control and motion editing. Finally, we discuss the limitations of the current state‐of‐the‐art methods based on DL and/or DRL in skeletal human character animation and possible directions of future research to alleviate current limitations and meet animators' needs.

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Fast Terrain-Adaptive Motion Generation using Deep Neural Networks

Cited by 2 publications

References 6 publications

Diverse Motion In-betweening from Sparse Keyframes with Dual Posture Stitching

Diverse Motion In-betweening from Sparse Keyframes with Dual Posture Stitching

A Survey on Deep Learning for Skeleton‐Based Human Animation

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