Hi-RQCT: Hierarchical Residual-Quantized Causal Transformer for High-Quality 3D Human Motion Generation

Recent advances in transformer-based text-to-motion generation have significantly improved motion quality. However, achieving both real-time performance and long-horizon scalability remains an open challenge. In this paper, we present Hi-RQCT (Hierarchical Residual-Quantized Causal Transformer), which generates high-quality lifelike 3D human motions by training a single transformer model. Hi-RQCT consists of only two main components: 1) RVQ-VAE, a hierarchical residual vector quantization variational autoencoder, which discretizes continuous motion sequences with high precision; 2) Hierarchical Causal Transformer, responsible for generating the base motion sequences in an autoregressive manner while simultaneously inferring residuals across different layers. Experimental results demonstrate that Hi-RQCT can generate smooth and continuous motion sequences up to 260 frames (13 seconds), surpassing the 196 frames (10 seconds) length limitation of existing datasets like HumanML3D. On the HumanML3D test set, our model achieves the best quantitative performance, and the generated motions also exhibit highly realistic and expressive visual quality in qualitative evaluations. © 2025 Copyright held by the owner/author(s).