DART: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control

• URL: http://arxiv.org/abs/2410.05260v1
• Date: Mon, 7 Oct 2024 17:58:22 GMT
• Title: DART: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control
• Authors: Kaifeng Zhao, Gen Li, Siyu Tang,
• Abstract summary: Text-conditioned human motion generation allows for user interaction through natural language. DART is a Diffusion-based Autoregressive motion primitive model for Real-time Text-driven motion control. We present effective algorithms for both approaches, demonstrating our model's versatility and superior performance in various motion synthesis tasks.
• Score: 12.465927271402442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-conditioned human motion generation, which allows for user interaction through natural language, has become increasingly popular. Existing methods typically generate short, isolated motions based on a single input sentence. However, human motions are continuous and can extend over long periods, carrying rich semantics. Creating long, complex motions that precisely respond to streams of text descriptions, particularly in an online and real-time setting, remains a significant challenge. Furthermore, incorporating spatial constraints into text-conditioned motion generation presents additional challenges, as it requires aligning the motion semantics specified by text descriptions with geometric information, such as goal locations and 3D scene geometry. To address these limitations, we propose DART, a Diffusion-based Autoregressive motion primitive model for Real-time Text-driven motion control. Our model, DART, effectively learns a compact motion primitive space jointly conditioned on motion history and text inputs using latent diffusion models. By autoregressively generating motion primitives based on the preceding history and current text input, DART enables real-time, sequential motion generation driven by natural language descriptions. Additionally, the learned motion primitive space allows for precise spatial motion control, which we formulate either as a latent noise optimization problem or as a Markov decision process addressed through reinforcement learning. We present effective algorithms for both approaches, demonstrating our model's versatility and superior performance in various motion synthesis tasks. Experiments show our method outperforms existing baselines in motion realism, efficiency, and controllability. Video results are available on the project page: https://zkf1997.github.io/DART/.

Related papers

• MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations [85.85596165472663]
  We build MotionBank, which comprises 13 video action datasets, 1.24M motion sequences, and 132.9M frames of natural and diverse human motions. Our MotionBank is beneficial for general motion-related tasks of human motion generation, motion in-context generation, and motion understanding.
  arXiv  Detail & Related papers  (2024-10-17T17:31:24Z)
• Infinite Motion: Extended Motion Generation via Long Text Instructions [51.61117351997808]
  "Infinite Motion" is a novel approach that leverages long text to extended motion generation. Key innovation of our model is its ability to accept arbitrary lengths of text as input. We incorporate the timestamp design for text which allows precise editing of local segments within the generated sequences.
  arXiv  Detail & Related papers  (2024-07-11T12:33:56Z)
• DiffusionPhase: Motion Diffusion in Frequency Domain [69.811762407278]
  We introduce a learning-based method for generating high-quality human motion sequences from text descriptions. Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space.
  arXiv  Detail & Related papers  (2023-12-07T04:39:22Z)
• LivePhoto: Real Image Animation with Text-guided Motion Control [51.31418077586208]
  This work presents a practical system, named LivePhoto, which allows users to animate an image of their interest with text descriptions. We first establish a strong baseline that helps a well-learned text-to-image generator (i.e., Stable Diffusion) take an image as a further input. We then equip the improved generator with a motion module for temporal modeling and propose a carefully designed training pipeline to better link texts and motions.
  arXiv  Detail & Related papers  (2023-12-05T17:59:52Z)
• Story-to-Motion: Synthesizing Infinite and Controllable Character Animation from Long Text [14.473103773197838]
  A new task, Story-to-Motion, arises when characters are required to perform specific motions based on a long text description. Previous works in character control and text-to-motion have addressed related aspects, yet a comprehensive solution remains elusive. We propose a novel system that generates controllable, infinitely long motions and trajectories aligned with the input text.
  arXiv  Detail & Related papers  (2023-11-13T16:22:38Z)
• AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism [24.049207982022214]
  We propose textbftT2M, a two-stage method with multi-perspective attention mechanism. Our method outperforms the current state-of-the-art in terms of qualitative and quantitative evaluation.
  arXiv  Detail & Related papers  (2023-09-02T02:18:17Z)
• 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)
• 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)
• MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model [35.32967411186489]
  MotionDiffuse is a diffusion model-based text-driven motion generation framework. It excels at modeling complicated data distribution and generating vivid motion sequences. It responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts.
  arXiv  Detail & Related papers  (2022-08-31T17:58:54Z)

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.