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Strong and Controllable 3D Motion Generation

Canxuan Gang

TL;DR

This work tackles real-time, text-driven 3D motion generation by addressing two core issues: inference efficiency and fine-grained joint control. It introduces two primary innovations: an Efficient Motion Transformer that uses flash linear attention to achieve linear computational complexity within the latent diffusion framework, and Motion ControlNet combined with latent consistency distillation to provide precise joint-level control in the motion latent space. Together, these components aim to accelerate motion generation while enabling controllable, high-fidelity motions suitable for real-world applications in gaming, robotics, and AR/VR. The authors outline a detailed plan encompassing literature review, implementation, comparative and ablation studies on HumanML3D, and manuscript preparation for top venues, underlining the practical impact and potential for real-time, controllable motion synthesis.

Abstract

Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative models or autoregressive models for text-to-motion generation. However, they face two significant challenges: (1) The generation process is time-consuming, posing a major obstacle for real-time applications such as gaming, robot manipulation, and other online settings. (2) These methods typically learn a relative motion representation guided by text, making it difficult to generate motion sequences with precise joint-level control. These challenges significantly hinder progress and limit the real-world application of human motion generation techniques. To address this gap, we propose a simple yet effective architecture consisting of two key components. Firstly, we aim to improve hardware efficiency and computational complexity in transformer-based diffusion models for human motion generation. By customizing flash linear attention, we can optimize these models specifically for generating human motion efficiently. Furthermore, we will customize the consistency model in the motion latent space to further accelerate motion generation. Secondly, we introduce Motion ControlNet, which enables more precise joint-level control of human motion compared to previous text-to-motion generation methods. These contributions represent a significant advancement for text-to-motion generation, bringing it closer to real-world applications.

Strong and Controllable 3D Motion Generation

TL;DR

This work tackles real-time, text-driven 3D motion generation by addressing two core issues: inference efficiency and fine-grained joint control. It introduces two primary innovations: an Efficient Motion Transformer that uses flash linear attention to achieve linear computational complexity within the latent diffusion framework, and Motion ControlNet combined with latent consistency distillation to provide precise joint-level control in the motion latent space. Together, these components aim to accelerate motion generation while enabling controllable, high-fidelity motions suitable for real-world applications in gaming, robotics, and AR/VR. The authors outline a detailed plan encompassing literature review, implementation, comparative and ablation studies on HumanML3D, and manuscript preparation for top venues, underlining the practical impact and potential for real-time, controllable motion synthesis.

Abstract

Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative models or autoregressive models for text-to-motion generation. However, they face two significant challenges: (1) The generation process is time-consuming, posing a major obstacle for real-time applications such as gaming, robot manipulation, and other online settings. (2) These methods typically learn a relative motion representation guided by text, making it difficult to generate motion sequences with precise joint-level control. These challenges significantly hinder progress and limit the real-world application of human motion generation techniques. To address this gap, we propose a simple yet effective architecture consisting of two key components. Firstly, we aim to improve hardware efficiency and computational complexity in transformer-based diffusion models for human motion generation. By customizing flash linear attention, we can optimize these models specifically for generating human motion efficiently. Furthermore, we will customize the consistency model in the motion latent space to further accelerate motion generation. Secondly, we introduce Motion ControlNet, which enables more precise joint-level control of human motion compared to previous text-to-motion generation methods. These contributions represent a significant advancement for text-to-motion generation, bringing it closer to real-world applications.

Paper Structure

This paper contains 15 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Objectives
  4. Proposed Method
  5. Efficient Motion Transformer
  6. Motion ControlNet and Latent Consistency Model
  7. Detailed Plans
  8. Literature Review
  9. Baseline and Preparation
  10. Design and Implement Efficient Motion Transformer
  11. Design and Implement Motion ControlNet and Latent Consistency Model
  12. Comparative and Ablation Studies
  13. Paper Writing
  14. Revision of Paper
  15. Acknowledgements

Figures (2)

  • Figure 2: Comparison of Attention Mechanisms. (a) Traditional Flash Attention with quadratic complexity. (b) and (c) Flash Linear Attention, showcasing its hardware-aware design and linear complexity due to the gated attention mechanism.
  • Figure 3: (a) Motion Latent Consistency Model : A consistency model customized for motion latent space, facilitating accurate prediction of clean latents through noise diffusion and network consistency. (b) Motion ControlNet: Integrated within the motion latent space, it enables controllable motion generation by supervising spatial control signals using decoded motion information.