T2LM: Long-Term 3D Human Motion Generation from Multiple Sentences

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

T2LM is a novel method for generating long-term 3D human motion from sequential text input, detailed in a CVPR 2024 submission. This continuous generation framework produces sequences of individual actions described by text, ensuring smooth transitions between them. A key innovation is its ability to operate without requiring training on long-term sequential datasets, distinguishing it from current methods that often need post-processing for realistic transitions and extensive long-term motion data for training. T2LM accepts raw input text and can generate infinite sequences of human motion at test time, offering a simple, on-the-fly inference solution. Its capabilities make it suitable for applications in embodied AI, such as training mobile robot navigation systems in simulators like Habitat, and for animating avatars and creating synthetic content in AR/VR environments.

Key takeaway

For Machine Learning Engineers developing embodied AI or AR/VR applications, T2LM offers a significant advancement in human motion generation. You can leverage this method to create realistic, long-term 3D human movements from raw text inputs, eliminating the need for extensive long-term training datasets or complex post-processing for transitions. Consider integrating T2LM for animating virtual agents in simulators like Habitat or for dynamic avatar control in immersive experiences, streamlining content creation and enhancing realism.

Key insights

T2LM generates smooth, long-term 3D human motion from raw text without needing long-term training data or post-processing.

Principles

• Smooth transitions are achievable without post-processing.
• Long-term motion generation can bypass long-term sequence training.
• Raw text input can directly condition complex motion.

Method

T2LM is a continuous long-term generation framework that creates individual text-described actions and smoothly connects them, operating on-the-fly at inference.

In practice

• Animate avatars in AR/VR using raw text commands.
• Generate human motion for robot navigation simulators.
• Create synthetic content for virtual environments.

Topics

• 3D Human Motion Generation
• Text-to-Motion
• Embodied AI
• AR/VR Animation
• Robot Navigation
• Generative Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.