T2M Mamba: Motion Periodicity-Saliency Coupling Approach for Stable Text-Driven Motion Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2610.13549v1 Announce Type: new Abstract: Text-guided long-term human motion generation is essential for applications such as continuously operating digital avatars. Despite recent progress on...
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:2608.24334v1 Announce Type: new Abstract: Discrete motion representations have substantially advanced autoregressive text-to-motion generation. However, most motion tokenizers are optimized for...
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.
Diffusion-based text-to-motion models synthesize realistic human motions but often exhibit semantic drift from the input text. Motion is inherently temporal, especially in compositional and long-duration sequences that require semantic consistency across multiple action segments and smooth kinematic transitions throughout the trajectory.
arXiv:2606. 06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding.