DiffusionPhase: Motion Diffusion in Frequency Domain

• URL: http://arxiv.org/abs/2312.04036v1
• Date: Thu, 7 Dec 2023 04:39:22 GMT
• Title: DiffusionPhase: Motion Diffusion in Frequency Domain
• Authors: Weilin Wan, Yiming Huang, Shutong Wu, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu
• Abstract summary: 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.
• Score: 69.811762407278
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e.g., ``A person walks forward"). Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences, due to limited text-to-motion datasets and the pose representations used that often lack expressiveness or compactness. To address these issues, we propose the first method for text-conditioned human motion generation in the frequency domain of motions. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space with high-frequency details encoded, capturing the local periodicity of motions in time and space with high accuracy. We also introduce a conditional diffusion model for predicting periodic motion parameters based on text descriptions and a start pose, efficiently achieving smooth transitions between motion sequences associated with different text descriptions. Experiments demonstrate that our approach outperforms current methods in generating a broader variety of high-quality motions, and synthesizing long sequences with natural transitions.

Related papers

• DART: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control [12.465927271402442]
  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.
  arXiv  Detail & Related papers  (2024-10-07T17:58:22Z)
• 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)
• Seamless Human Motion Composition with Blended Positional Encodings [38.85158088021282]
  We introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without postprocessing or redundant denoising steps. We achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets.
  arXiv  Detail & Related papers  (2024-02-23T18:59:40Z)
• 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)
• Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling [74.62570964142063]
  Text-to-motion generation has gained increasing attention, but most existing methods are limited to generating short-term motions. We propose a novel approach that utilizes a past-conditioned diffusion model with two optional coherent sampling methods. Our proposed method is capable of generating compositional and coherent long-term 3D human motions controlled by a user-instructed long text stream.
  arXiv  Detail & Related papers  (2023-08-03T16:18:32Z)
• 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)
• Executing your Commands via Motion Diffusion in Latent Space [51.64652463205012]
  We propose a Motion Latent-based Diffusion model (MLD) to produce vivid motion sequences conforming to the given conditional inputs. Our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks.
  arXiv  Detail & Related papers  (2022-12-08T03:07:00Z)

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.