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20232026
most citedTemporal Consistency-Aware Text-to-Motion Generation

2 citations · 3 across the 10 of their papers we have counts for

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cs.CV2026

MotionRFT: Unified Reinforcement Fine-Tuning for Text-to-Motion Generation

Xiaofeng Tan, Wanjiang Weng, Hongsong Wang +3

Text-to-motion generation has advanced with diffusion- and flow-based generative models, yet supervised pre-training remains insufficient to align models with high-level objectives…

cs.CV2026★ 2 cited

Temporal Consistency-Aware Text-to-Motion Generation

Hongsong Wang, Wenjing Yan, Qiuxia Lai +1

Text-to-Motion (T2M) generation aims to synthesize realistic human motion sequences from natural language descriptions. While two-stage frameworks leveraging discrete motion repres…