computer-code-4

Motion Style Transfer

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Motion Puzzle: Arbitrary Motion Style Transfer by Body Part

Deok-Kyeong Jang, S. Park, Sung-Hee Lee•Wed Feb 09 2022

The Motion Puzzle is the first that can control the motion style of individual body parts, allowing for local style editing and significantly increasing the range of stylized motions.

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Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Alexandre Alahi, Yuejiang Liu, Parth Kothari, Danyang Li•Sat Nov 05 2022

This work proposes a transfer learning approach for efficiently adapting pre-trained forecasting models to new domains, such as unseen agent types and scene contexts, and introduces two components that exploit prior knowledge of motion style shifts, including a low-rank motion style adapter and a modular adapter strategy.

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Unpaired motion style transfer from video to animation

Kfir Aberman, Dani Lischinski, Baoquan Chen, D. Cohen-Or, Yijia Weng•Mon May 11 2020

This paper presents a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training, and is the first to demonstrate style transfer directly from videos to 3D animations.

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Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization

Makito Kobayashi, Chen-Chieh Liao, Keito Inoue, Sentaro Yojima, Masafumi Takahashi•Wed Jun 14 2023

This work proposes a new style-diverse dataset that uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects and claims the challenges in motion style transfer.

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MoST: Motion Style Transformer Between Diverse Action Contents

Boeun Kim, Jungho Kim, Hyung Jin Chang, J. Choi•Sat Mar 09 2024

A novel motion style transformer that effectively disentangles style from content and generates a plausible motion with transferred style from a source motion without the need for heuristic post-processing is proposed.

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