Text-to-Motion Generation

Text-to-motion generation is a rapidly growing field at the nexus of multimodal learning and computer graphics, enabling flexible and cost-effective applications in gaming, animation, robotics, and virtual reality. Existing part-aware pipelines typically split the body into segments and then recover whole-body coordination via attention-based fusion, which incurs quadratic complexity and fails to model part-level and whole-body cues simultaneously. Moreover, we observe that naively stacking spatial and temporal modules accumulates redundant features, thereby requiring deeper networks to achieve competitive fidelity. To address these issues, we propose HiSTF Mamba, a linear-time framework composed of Dual-Spatial Mamba, Bi-Temporal Mamba, and the Dynamic Spatiotemporal Fusion Module (DSFM). Dual-Spatial Mamba performs parallel part-based and whole-based modeling to simultaneously capture local joint dynamics and global coordination. Bi-Temporal Mamba employs bidirectional scanning to encode both short-term motion details and long-term dependencies. DSFM further removes redundancy and extracts complementary temporal information before fusing it with spatial features, yielding an expressive spatiotemporal representation. Extensive experiments on HumanML3D and KIT-ML demonstrate that HiSTF Mamba improves generation fidelity and semantic alignment while requiring substantially fewer parameters than existing segment-based architectures.

Generating natural and expressive human motions from textual descriptions is challenging due to the complexity of coordinating full-body dynamics and capturing nuanced motion patterns over extended sequences that accurately reflect the given text. To address this, we introduce BiPO, Bidirectional Partial Occlusion Network for Text-to-Motion Synthesis, a novel model that enhances text-to-motion synthesis by integrating part-based generation with a bidirectional autoregressive architecture. This integration allows BiPO to consider both past and future contexts during generation while enhancing detailed control over individual body parts without requiring ground-truth motion length. To relax the interdependency among body parts caused by the integration, we devise the Partial Occlusion technique, which probabilistically occludes the certain motion part information during training. In our comprehensive experiments, BiPO achieves state-of-the-art performance on the HumanML3D dataset, outperforming recent methods such as ParCo, MoMask, and BAMM in terms of FID scores and overall motion quality. Notably, BiPO excels not only in the text-to-motion generation task but also in motion editing tasks that synthesize motion based on partially generated motion sequences and textual descriptions. These results reveal the BiPO's effectiveness in advancing text-to-motion synthesis and its potential for practical applications.

Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion methods have been proposed due to their high sample quality and expressiveness. Early approaches treat the entire sequence as a whole, which is computationally expensive and restricts sequence length. In contrast, autoregressive diffusion models generate longer sequences. However, their reliance on fully denoising previous frames complicates training and inference. Consequently, we propose RDM, a new recurrent diffusion formulation inspired by Recurrent Neural Networks (RNNs). RDM explicitly conditions the diffusion process on preceding noisy frames, avoiding the cost of full denoising. To overcome the challenge of maintaining its probabilistic nature, we employ Normalizing Flows to model recurrent connections. Our evaluations demonstrate RDM's effectiveness: it achieves comparable performance to autoregressive baselines and generates long sequences that remain aligned with the text. RDM also skips diffusion steps during inference, reducing computational cost by up to 18x over autoregressive baselines. Project page: https://www.mirgahney.com/rfdm.io/.