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T2LM generates long-term 3D human motion from text

By PulseAugur Editorial · [1 sources] ·

Researchers have developed T2LM, a novel framework for generating long sequences of 3D human motion from multiple sentences. Unlike previous methods that required sequential motion data and often produced unrealistic gaps, T2LM can be trained without such data. It utilizes a VQ-VAE to compress motion into latent vectors and a Transformer-based text encoder to predict these vectors from text, enabling smoother transitions and improved motion generation. AI

IMPACT Enables more realistic and extended 3D character animations for gaming, film, and virtual environments.

RANK_REASON This is a research paper detailing a new method for 3D human motion generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Taeryung Lee, Fabien Baradel, Thomas Lucas, Kyoung Mu Lee, Gregory Rogez ·

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

    arXiv:2406.00636v2 Announce Type: replace Abstract: In this paper, we address the challenging problem of long-term 3D human motion generation. Specifically, we aim to generate a long sequence of smoothly connected actions from a stream of multiple sentences (i.e., paragraph). Pre…

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