SMGDiff: Soccer Motion Generation using diffusion probabilistic models

• URL: http://arxiv.org/abs/2411.16216v1
• Date: Mon, 25 Nov 2024 09:25:53 GMT
• Title: SMGDiff: Soccer Motion Generation using diffusion probabilistic models
• Authors: Hongdi Yang, Chengyang Li, Zhenxuan Wu, Gaozheng Li, Jingya Wang, Jingyi Yu, Zhuo Su, Lan Xu,
• Abstract summary: Soccer is a globally renowned sport with significant applications in video games and VR/AR. In this paper, we introduce SMGDiff, a novel two-stage framework for generating real-time and user-controllable soccer motions. Our key idea is to integrate real-time character control with a powerful diffusion-based generative model, ensuring high-quality and diverse output motion.
• Score: 44.54275548434197
• License:
• Abstract: Soccer is a globally renowned sport with significant applications in video games and VR/AR. However, generating realistic soccer motions remains challenging due to the intricate interactions between the human player and the ball. In this paper, we introduce SMGDiff, a novel two-stage framework for generating real-time and user-controllable soccer motions. Our key idea is to integrate real-time character control with a powerful diffusion-based generative model, ensuring high-quality and diverse output motion. In the first stage, we instantly transform coarse user controls into diverse global trajectories of the character. In the second stage, we employ a transformer-based autoregressive diffusion model to generate soccer motions based on trajectory conditioning. We further incorporate a contact guidance module during inference to optimize the contact details for realistic ball-foot interactions. Moreover, we contribute a large-scale soccer motion dataset consisting of over 1.08 million frames of diverse soccer motions. Extensive experiments demonstrate that our SMGDiff significantly outperforms existing methods in terms of motion quality and condition alignment.

Related papers

• TranSPORTmer: A Holistic Approach to Trajectory Understanding in Multi-Agent Sports [28.32714256545306]
  TranSPORTmer is a unified transformer-based framework capable of addressing all these tasks. It effectively captures temporal dynamics and social interactions in an equivariant manner. It outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation.
  arXiv  Detail & Related papers  (2024-10-23T11:35:44Z)
• ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model [9.525806425270428]
  We present emphReinDiffuse that combines reinforcement learning with motion diffusion model to generate physically credible human motions. Our method adapts Motion Diffusion Model to output a parameterized distribution of actions, making them compatible with reinforcement learning paradigms. Our approach outperforms existing state-of-the-art models on two major datasets, HumanML3D and KIT-ML.
  arXiv  Detail & Related papers  (2024-10-09T16:24:11Z)
• MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls [30.487510829107908]
  We propose MotionCraft, a unified diffusion transformer that crafts whole-body motion with plug-and-play multimodal control. Our framework employs a coarse-to-fine training strategy, starting with the first stage of text-to-motion semantic pre-training. We introduce MC-Bench, the first available multimodal whole-body motion generation benchmark based on the unified SMPL-X format.
  arXiv  Detail & Related papers  (2024-07-30T18:57:06Z)
• Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning [17.906144781244336]
  We train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This paper constitutes a first demonstration of end-to-end training for multi-agent robot soccer.
  arXiv  Detail & Related papers  (2024-05-03T18:41:13Z)
• Large Motion Model for Unified Multi-Modal Motion Generation [50.56268006354396]
  Large Motion Model (LMM) is a motion-centric, multi-modal framework that unifies mainstream motion generation tasks into a generalist model. LMM tackles these challenges from three principled aspects.
  arXiv  Detail & Related papers  (2024-04-01T17:55:11Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• Priority-Centric Human Motion Generation in Discrete Latent Space [59.401128190423535]
  We introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM) for text-to-motion generation. M2DM incorporates a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token.
  arXiv  Detail & Related papers  (2023-08-28T10:40:16Z)
• Human MotionFormer: Transferring Human Motions with Vision Transformers [73.48118882676276]
  Human motion transfer aims to transfer motions from a target dynamic person to a source static one for motion synthesis. We propose Human MotionFormer, a hierarchical ViT framework that leverages global and local perceptions to capture large and subtle motion matching. Experiments show that our Human MotionFormer sets the new state-of-the-art performance both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2023-02-22T11:42:44Z)
• UniCon: Universal Neural Controller For Physics-based Character Motion [70.45421551688332]
  We propose a physics-based universal neural controller (UniCon) that learns to master thousands of motions with different styles by learning on large-scale motion datasets. UniCon can support keyboard-driven control, compose motion sequences drawn from a large pool of locomotion and acrobatics skills and teleport a person captured on video to a physics-based virtual avatar.
  arXiv  Detail & Related papers  (2020-11-30T18:51:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.