arXiv:2607. 22702v1 Announce Type: cross Abstract: Text-motion representation learning has advanced rapidly, with growing interest in multi person interactions for animation, AR/VR, and embodied AI.
By Addison Zucek, Prerit Gupta, Kamila Kuatova, Aniket Bera
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.
The paper introduces a unified conditional-flow framework that integrates text-driven motion generation, semantic editing, and intra-structural retargeting into a single rectified-flow model. By treating editing as a change in semantic condition and retargeting as a change in skeletal condition, the approach eliminates fragmented pipelines and allows a single model to perform generation, zero‑shot editing, and zero‑shot retargeting on articulated 3D motion data. Experiments on SnapMoGen and a Mixamo subset demonstrate that the model can handle all three tasks without task‑specific fine‑tuning, preserving both motion semantics and skeletal structure.
By Junlin Li, Xinhao Song, Siqi Wang, Haibin Huang, Yili Zhao
arXiv:2603.08590v4 Announce Type: replace
Abstract: Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level lat...
By Zeyu Ling, Qing Shuai, Teng Zhang, Shiyang Li, Bo Han, Changqing Zou
arXiv:2609.14615v1 Announce Type: cross
Abstract: Unified motion generation and understanding is crucial for embodied AI systems that can both synthesize and interpret human actions in open-world env...
By Guocun Wang, Kenkun Liu, Guorui Song, Jing Lin, Zhe Huang, Luyuan Zhang, Dake Zhong, Choo Sin Wai, Xiaoguang Han, Haoqian Wang
ReMoMask-2 is a retrieval‑augmented text‑to‑motion generation framework that improves on complex motion descriptions by addressing coarse retrieval and representation gaps. It introduces a structure‑aware RAG pipeline with Hierarchical Bidirectional Momentum contrastive learning, Semantic Spatial‑Temporal Attention, and Topology Structured Masking, and rebuilds the retrieval database in the generator’s latent space using a lightweight projector. Experiments on HumanML3D, KIT‑ML, and SnapMoGen show state‑of‑the‑art retrieval accuracy and the lowest FID scores, with a single mask‑transformer stage delivering faster inference than the previous two‑stage design.