Xingzu Zhan

Xingzu Zhan, Chen Xie, Honghang Chen et al.

Text-to-motion generation, which converts motion language descriptions into coherent 3D human motion sequences, has attracted increasing attention in fields, such as avatar animation and humanoid robotic interaction. Though existing models have achieved significant fidelity, they still suffer from two core limitations: (i) They treat motion periodicity and keyframe saliency as independent factors, overlooking their coupling and causing generation drift in long sequences. (ii) They are fragile to semantically equivalent paraphrases, where minor synonym substitutions distort textual embeddings, propagating through the decoder and producing unstable or erroneous motions. In this work, we propose T2M Mamba to address these limitations by (i) proposing Periodicity-Saliency Aware Mamba, which utilizes novel algorithms for keyframe weight estimation via enhanced Density Peaks Clustering and motion periodicity estimation via FFT-accelerated autocorrelation to capture coupled dynamics with minimal computational overhead, and (ii) constructing a Periodic Differential Cross-modal Alignment Module (PDCAM) to enhance robust alignment of textual and motion embeddings. Extensive experiments on HumanML3D and KIT-ML datasets have been conducted, confirming the effectiveness of our approach, achieving an FID of 0.068 and consistent gains on all other metrics.

Xingzu Zhan, Chen Xie, Honghang Chen et al.

Text-to-motion generation sits at the intersection of multimodal learning and computer graphics and is gaining momentum because it can simplify content creation for games, animation, robotics and virtual reality. Most current methods stack spatial and temporal features in a straightforward way, which adds redundancy and still misses subtle joint-level cues. We introduce HiSTF Mamba, a framework with three parts: Dual-Spatial Mamba, Bi-Temporal Mamba and a Dynamic Spatiotemporal Fusion Module (DSFM). The Dual-Spatial module runs part-based and whole-body models in parallel, capturing both overall coordination and fine-grained joint motion. The Bi-Temporal module scans sequences forward and backward to encode short-term details and long-term dependencies. DSFM removes redundant temporal information, extracts complementary cues and fuses them with spatial features to build a richer spatiotemporal representation. Experiments on the HumanML3D benchmark show that HiSTF Mamba performs well across several metrics, achieving high fidelity and tight semantic alignment between text and motion.