Style-Conditioned Motion Generation

Latest papers

9 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
Oct 13, 2026Joshua Aurand, Agon Serifi, Sammy Christen +4Style-Conditioned Motion Generation Robot Locomotion
Disney Research Zürich, Switzerland · ETH Zürich, Switzerland
VICAR: Video Imitation with Contact-Aware Retargeting for Humanoid Interactive Skills
Oct 9, 2026Dvij Kalaria, Bike Zhang, S. Shankar SastryStyle-Conditioned Motion Generation Robot Imitation Learning
University of California, Berkeley
FlowHMR: Physically Plausible Motion Capture from Video
Oct 2, 2026Zhanke Wang, Chengfeng Zhao, Qing Shuai +8Style-Conditioned Motion Generation 3D Human Motion Reconstruction
Peking University · The Hong Kong University of Science and Technology · Tencent +3
GestAdapt: Workspace-Conditioned Co-Speech Gesture Generation for Humanoid Robots
Sep 29, 2026Bosong Ding, Xianglin Zhang, Miao Xin +2Robot Motion Generation Style-Conditioned Motion Generation
Department of Intelligent Systems, Tilburg University, The Netherlands. · TU-Dresden, Dresden, Germany. · School of Artificial Intelligence, China University of Mining and Technology-Beijing (CUMTB), Beijing, China.
Flexible Motion Generation from Language and Style References
Sep 7, 2026Kai Weixian Lan, Bodie Criswell, Briana Fedkiw +3Text-to-Motion Generation Motion Diffusion Models
University of California, Davis, USA · University of Utah, USA · Epic Games, USA +1
Prior-First, Condition-Second: Scalable and Controllable Hand Motion Completion
Jul 7, 2026Mingyi Shi, Xuelin Chen, Taku KomuraHuman Motion Generation Style-Conditioned Motion Generation
The University of Hong Kong, Hong Kong · Adobe Research, London, UK
HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching
Apr 12, 2026Zerui Chen, Rolandos Alexandros Potamias, Shizhe Chen +3Style-Conditioned Motion Generation HOI Generation
Imperial College London · Inria, CNRS, ENS-PSL
Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow Distillation
Mar 2, 2026Divyanshu Daiya, Aniket BeraMarkov Chains Style-Conditioned Motion Generation
IDEAS Lab, Department of Computer Science, Purdue University
Robo-Saber: Generating and Simulating Virtual Reality Players
Feb 20, 2026Nam Hee Kim, Jingjing May Liu, Jaakko Lehtinen +3Human Behavior Simulation Human Motion Generation
Aalto University, Finland · University of California, Berkeley, United States · NVIDIA +1

Oct 13, 2026J/K move · Enter open · S save

Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion

Joshua Aurand, Agon Serifi, Sammy Christen, Ruben Grandia+3

Disney Research Zürich, Switzerland · ETH Zürich, Switzerland

Style-Conditioned Motion Generation Robot Locomotion Humanoid Robot Locomotion Human Motion Generation Diffusion Models for Motion Generation

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We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven controllers, which typically require explicit style labels for training, our method efficiently leverages large-scale unstructured motion data and generalizes to unseen styles. We disentangle style from functional locomotion through a tailored sampling strategy used to train a style encoder whose output conditions an autoregressive diffusion model. During inference, the user provides a style reference clip and a joystick velocity command. The diffusion model then generates a kinematic reference tracked by a reinforcement learning controller. To improve user controllability and stylization strength, we propose an attention biasing mechanism and a velocity command correction strategy. We demonstrate our method on a physics-based humanoid character in simulation and on a physical robot, showcasing robust real-time control and continuous style modulation. The results can be best seen in our supplemental video (https://www.youtube.com/watch?v=roFnS6N7nJ4).