FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation
- URL: http://arxiv.org/abs/2501.16778v1
- Date: Tue, 28 Jan 2025 08:02:21 GMT
- Title: FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation
- Authors: Arvin Tashakori, Arash Tashakori, Gongbo Yang, Z. Jane Wang, Peyman Servati,
- Abstract summary: We propose a novel framework that leverages a computationally lightweight diffusion model operating in the latent space.
FlexMotion employs a multimodal pre-trained Transformer encoder-decoder, integrating joint locations, contact forces, joint actuations and muscle activations.
We evaluate FlexMotion on extended datasets and demonstrate its superior performance in terms of realism, physical plausibility, and controllability.
- Score: 12.60677181866807
- License:
- Abstract: Lightweight, controllable, and physically plausible human motion synthesis is crucial for animation, virtual reality, robotics, and human-computer interaction applications. Existing methods often compromise between computational efficiency, physical realism, or spatial controllability. We propose FlexMotion, a novel framework that leverages a computationally lightweight diffusion model operating in the latent space, eliminating the need for physics simulators and enabling fast and efficient training. FlexMotion employs a multimodal pre-trained Transformer encoder-decoder, integrating joint locations, contact forces, joint actuations and muscle activations to ensure the physical plausibility of the generated motions. FlexMotion also introduces a plug-and-play module, which adds spatial controllability over a range of motion parameters (e.g., joint locations, joint actuations, contact forces, and muscle activations). Our framework achieves realistic motion generation with improved efficiency and control, setting a new benchmark for human motion synthesis. We evaluate FlexMotion on extended datasets and demonstrate its superior performance in terms of realism, physical plausibility, and controllability.
Related papers
- X-Dyna: Expressive Dynamic Human Image Animation [49.896933584815926]
X-Dyna is a zero-shot, diffusion-based pipeline for animating a single human image.
It generates realistic, context-aware dynamics for both the subject and the surrounding environment.
arXiv Detail & Related papers (2025-01-17T08:10:53Z) - A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions [56.709280823844374]
We introduce a mask-based motion correction module (MCM) that leverages motion context and video mask to repair flawed motions.
We also propose a physics-based motion transfer module (PTM), which employs a pretrain and adapt approach for motion imitation.
Our approach is designed as a plug-and-play module to physically refine the video motion capture results, including high-difficulty in-the-wild motions.
arXiv Detail & Related papers (2024-12-23T08:26:00Z) - Morph: A Motion-free Physics Optimization Framework for Human Motion Generation [25.51726849102517]
Our framework achieves state-of-the-art motion generation quality while improving physical plausibility drastically.
Experiments on text-to-motion and music-to-dance generation tasks demonstrate that our framework achieves state-of-the-art motion generation quality.
arXiv Detail & Related papers (2024-11-22T14:09:56Z) - Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes [90.39860012099393]
Sitcom-Crafter is a system for human motion generation in 3D space.
Central to the function generation modules is our novel 3D scene-aware human-human interaction module.
Augmentation modules encompass plot comprehension for command generation, motion synchronization for seamless integration of different motion types.
arXiv Detail & Related papers (2024-10-14T17:56:19Z) - 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) - FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis [65.85686550683806]
This paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution.
Based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion.
arXiv Detail & Related papers (2024-05-24T17:57:57Z) - DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics [21.00283279991885]
We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics.
We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.
arXiv Detail & Related papers (2023-09-24T20:25:59Z) - MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis [73.52948992990191]
MoFusion is a new denoising-diffusion-based framework for high-quality conditional human motion synthesis.
We present ways to introduce well-known kinematic losses for motion plausibility within the motion diffusion framework.
We demonstrate the effectiveness of MoFusion compared to the state of the art on established benchmarks in the literature.
arXiv Detail & Related papers (2022-12-08T18:59:48Z) - 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.