arXiv Computer Vision
SeMoCo: A Semantic-First Motion Codec for Motion Language Modeling
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arXiv AI
Sep 15
Open-UniMo: Towards Unified Motion-Language Understanding and Generation in the Open World
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...
arXiv Computer Vision
Sep 30
MotionMaestro: Masked Tokenization for Unified Motion Generation
arXiv:2609.37495v1 Announce Type: new Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exis...
arXiv Computer Vision
Sep 15
MoVT: Video-Augmented Motion Tokenizer for Text-to-Motion Generation
MoVT is a new framework for text‑to‑motion generation that uses a cross‑modal augmented motion tokenizer to project 3D motion tokens into 2D, enriching the motion codebook with real‑world video patterns. The enriched tokens are mapped back to 3D, creating aligned 3D and 2D codebooks that better capture intricate motions. These codebooks feed a generative masked transformer, which predicts masked motion tokens in a modality‑agnostic way, allowing text‑index pairs from the 2D codebook and annotated videos to further improve generation quality. Empirical tests show MoVT outperforms previous state‑of‑the‑art methods on several key metrics.
arXiv Machine Learning
Jul 31
MUGEN: A Unified Framework for Efficient Motion Understanding and Generation
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.
arXiv Computer Vision
Sep 14
Uni-HOI:A Unified framework for Learning the Joint distribution of Text and Human-Object Interaction
Uni-HOI is a unified framework that learns the joint distribution among text, human motion, and object motion for 4D human‑object interaction (HOI). It uses large language models and two motion‑specific VQ‑VAEs to convert heterogeneous motion data into token sequences, enabling seamless integration of all three modalities. A two‑stage training strategy first captures correlations on a large‑scale HOI dataset and then fine‑tunes for specific tasks, achieving strong performance on text‑driven HOI generation, object‑motion‑driven human motion generation, and human‑motion‑driven object motion prediction.
arXiv Computer Vision
1d ago
T2M Mamba: Motion Periodicity-Saliency Coupling Approach for Stable Text-Driven Motion Generation
arXiv:2602.01352v2 Announce Type: replace Abstract: Text-to-motion generation, which converts motion language descriptions into coherent 3D human motion sequences, has attracted increasing attention...