Programmable Motion Generation for Open-Set Motion Control Tasks

• URL: http://arxiv.org/abs/2405.19283v1
• Date: Wed, 29 May 2024 17:14:55 GMT
• Title: Programmable Motion Generation for Open-Set Motion Control Tasks
• Authors: Hanchao Liu, Xiaohang Zhan, Shaoli Huang, Tai-Jiang Mu, Ying Shan,
• Abstract summary: We introduce a new paradigm, programmable motion generation. In this paradigm, any given motion control task is broken down into a combination of atomic constraints. These constraints are then programmed into an error function that quantifies the degree to which a motion sequence adheres to them.
• Score: 51.73738359209987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Character animation in real-world scenarios necessitates a variety of constraints, such as trajectories, key-frames, interactions, etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control tasks. They are often specialized, and the tasks they address are rarely extendable or customizable. We categorize these as solutions to the close-set motion control problem. In response to the complexity of practical motion control, we propose and attempt to solve the open-set motion control problem. This problem is characterized by an open and fully customizable set of motion control tasks. To address this, we introduce a new paradigm, programmable motion generation. In this paradigm, any given motion control task is broken down into a combination of atomic constraints. These constraints are then programmed into an error function that quantifies the degree to which a motion sequence adheres to them. We utilize a pre-trained motion generation model and optimize its latent code to minimize the error function of the generated motion. Consequently, the generated motion not only inherits the prior of the generative model but also satisfies the required constraints. Experiments show that we can generate high-quality motions when addressing a wide range of unseen tasks. These tasks encompass motion control by motion dynamics, geometric constraints, physical laws, interactions with scenes, objects or the character own body parts, etc. All of these are achieved in a unified approach, without the need for ad-hoc paired training data collection or specialized network designs. During the programming of novel tasks, we observed the emergence of new skills beyond those of the prior model. With the assistance of large language models, we also achieved automatic programming. We hope that this work will pave the way for the motion control of general AI agents.

Related papers

• MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding [76.30210465222218]
  MotionGPT-2 is a unified Large Motion-Language Model (LMLMLM) It supports multimodal control conditions through pre-trained Large Language Models (LLMs) It is highly adaptable to the challenging 3D holistic motion generation task.
  arXiv  Detail & Related papers  (2024-10-29T05:25:34Z)
• MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting [38.15158715821526]
  MaskedMimic is a novel approach that formulates physics-based character control as a general motion inpainting problem. By unifying character control through motion inpainting, MaskedMimic creates versatile virtual characters. These characters can dynamically adapt to complex scenes and compose diverse motions on demand, enabling more interactive and immersive experiences.
  arXiv  Detail & Related papers  (2024-09-22T11:10:59Z)
• CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation [44.9991846328409]
  Crowd Motion Generation is essential in entertainment industries such as animation and games as well as in strategic fields like urban simulation and planning. We introduce CrowdMoGen, a zero-shot text-driven framework that harnesses the power of Large Language Model (LLM) to incorporate the collective intelligence into the motion generation framework. Our framework consists of two key components: 1) Crowd Scene Planner that learns to coordinate motions and dynamics according to specific scene contexts or introduced perturbations, and 2) Collective Motion Generator that efficiently synthesizes the required collective motions.
  arXiv  Detail & Related papers  (2024-07-08T17:59:36Z)
• Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs [67.59291068131438]
  Motion-Agent is a conversational framework designed for general human motion generation, editing, and understanding. Motion-Agent employs an open-source pre-trained language model to develop a generative agent, MotionLLM, that bridges the gap between motion and text.
  arXiv  Detail & Related papers  (2024-05-27T09:57:51Z)
• CoMo: Controllable Motion Generation through Language Guided Pose Code Editing [57.882299081820626]
  We introduce CoMo, a Controllable Motion generation model, adept at accurately generating and editing motions. CoMo decomposes motions into discrete and semantically meaningful pose codes. It autoregressively generates sequences of pose codes, which are then decoded into 3D motions.
  arXiv  Detail & Related papers  (2024-03-20T18:11:10Z)
• 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)
• AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control [145.61135774698002]
  We propose a fully automated approach to selecting motion for a character to track in a given scenario. High-level task objectives that the character should perform can be specified by relatively simple reward functions. Low-level style of the character's behaviors can be specified by a dataset of unstructured motion clips. Our system produces high-quality motions comparable to those achieved by state-of-the-art tracking-based techniques.
  arXiv  Detail & Related papers  (2021-04-05T22:43:14Z)
• Character Controllers Using Motion VAEs [9.806910643086045]
  We learn data-driven generative models of human movement using Motion VAEs. Planning or control algorithms can then use this action space to generate desired motions.
  arXiv  Detail & Related papers  (2021-03-26T05:51:41Z)

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.