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T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations

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arxiv 2301.06052 v4 pith:XSSBR3XI submitted 2023-01-15 cs.CV

Jianrong Zhang , Yangsong Zhang , Xiaodong Cun , Shaoli Huang , Yong Zhang , Hongwei Zhao , Hongtao Lu , Xi Shen This is my paper · ORCID
classification cs.CV
keywords motionhumansimplevq-vaeapproachapproachescompetitivedataset
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In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions. We show that a simple CNN-based VQ-VAE with commonly used training recipes (EMA and Code Reset) allows us to obtain high-quality discrete representations. For GPT, we incorporate a simple corruption strategy during the training to alleviate training-testing discrepancy. Despite its simplicity, our T2M-GPT shows better performance than competitive approaches, including recent diffusion-based approaches. For example, on HumanML3D, which is currently the largest dataset, we achieve comparable performance on the consistency between text and generated motion (R-Precision), but with FID 0.116 largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach. Our work suggests that VQ-VAE still remains a competitive approach for human motion generation.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Distilling frozen Motion-JEPA features into a compact 32-D latent whose geometry is coupled to the decoder lets a standard non-autoregressive flow-matching DiT reach state-of-the-art text-to-motion quality on HumanML3...

  2. ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    ScaleMoGen introduces a scale-wise autoregressive framework that quantizes motions into hierarchical discrete tokens and predicts next-scale maps to achieve SOTA FID 0.030 on HumanML3D and text-guided editing.

  3. Absolute Coordinates Make Motion Generation Easy

    cs.CV 2025-05 conditional novelty 7.0 of 10

    Using absolute 3D joint coordinates with a plain Transformer and velocity-prediction diffusion outperforms the standard local-relative motion representation, improving fidelity and enabling direct control.

  4. Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling

    cs.MM 2025-07 conditional novelty 6.0 of 10

    A new captured music-dance dataset with facial expressions and a hierarchical residual VQ plus masked-transformer model that generates expressive 3D dance from music.

  5. HuMoCon: Concept Discovery for Human Motion Understanding

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A framework that combines explicit video-motion feature alignment with velocity-aware masked autoencoding to improve LLM-based human motion and video question answering.

  6. ANT: Adaptive Neural Temporal-Aware Text-to-Motion Model

    cs.CV 2025-06 conditional novelty 5.0 of 10

    ANT makes text embeddings change across denoising steps and schedules classifier-free guidance to decay, improving text-motion alignment in diffusion text-to-motion models.

  7. Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward

    cs.CV 2025-05 conditional novelty 3.0 of 10

    A survey paper reviews multimodal generative AI and autoregressive LLMs for text-driven human motion generation, with comparative tables of models, datasets, and metrics.

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