Linear Multi-granular Body Modeling for Text-Driven Motion Generation proposes HiSTF Mamba, a linear-time framework that combines Dual‑Spatial Mamba, Bi‑Temporal Mamba, and a Dynamic Spatiotemporal Fusion Module to model both part‑level and whole‑body dynamics. The approach captures local joint dynamics and global coordination in parallel, encodes short‑term and long‑term motion details bidirectionally, and removes redundant features to produce an expressive spatiotemporal representation. Experiments on HumanML3D and KIT‑ML show that HiSTF Mamba achieves higher generation fidelity and semantic alignment while using far fewer parameters than existing segment‑based architectures.
By Xingzu Zhan, Genpei Zhang, Chen Xie, Honghang Chen, Haoran Sun, Xiaochun Mai
arXiv:2603. 22282v2 Announce Type: replace-cross Abstract: We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture.
By Ziyi Wang, Xinshun Wang, Shuang Chen, Yang Cong, Mengyuan Liu
BiMoGen introduces a unified masked discrete diffusion framework for bidirectional motion‑text generation, addressing limitations of autoregressive models by allowing iterative bidirectional prediction. The method employs a two‑stage training strategy—Decoupled Uni‑ and Cross‑Modal Training followed by supervised fine‑tuning—to establish cross‑modal correspondence and specialize for bidirectional generation. To mitigate train‑test mismatch, Generation‑Aware Self‑Correction exposes the model to its own predictions during training and applies correction passes early in sampling to refine unmasked tokens, achieving competitive results on HumanML3D and KIT‑ML.
By Wanjiang Weng, Yongliang Wu, Xiaofeng Tan, Xingyu Zhu, Wenbo Zhu, Hongsong Wang
The paper introduces Timo, a kinematics-aware multimodal diffusion transformer designed for human motion generation. Timo employs fully shared multimodal attention, flow matching, and geometric/rotational-kinematics supervision to better coordinate articulated motion, and uses a two-stage curriculum to align motion with text captions. The authors also present a new benchmark of 40,025 clips from six datasets, showing that Timo outperforms state‑of‑the‑art methods, achieving a 40.8% relative improvement over Kimodo on average.
By Zhao Wang, Jiangtao Hu, Jack Yu, Tao Yu
BiMoGen introduces a unified masked discrete diffusion framework for bidirectional motion‑text generation, addressing the limitations of autoregressive models in capturing bidirectional dependencies between language and motion. The approach employs a two‑stage training strategy—decoupled uni‑ and cross‑modal pretraining followed by supervised fine‑tuning—to establish robust cross‑modal correspondence, and incorporates Generation‑Aware Self‑Correction to mitigate error propagation during inference. Experiments on HumanML3D and KIT‑ML show competitive performance on both text‑to‑motion and motion‑to‑text tasks, demonstrating the effectiveness of the proposed training and correction mechanisms.
arXiv:2607. 27581v1 Announce Type: new Abstract: Grounding human motion in language, and language in motion, is a central step toward physical AI systems that can understand, generate, and communicate human behavior.
By Zhankai Ye, Yukai Jin, Bingyang Wei, Bofan Li, Yusen Wu, Fangyi Li, Shangqian Gao, Xin Liu