HiSTF Mamba: Hierarchical Spatiotemporal Fusion with Multi-Granular Body-Spatial Modeling for High-Fidelity Text-to-Motion Generation

• URL: http://arxiv.org/abs/2503.06897v1
• Date: Mon, 10 Mar 2025 04:01:48 GMT
• Title: HiSTF Mamba: Hierarchical Spatiotemporal Fusion with Multi-Granular Body-Spatial Modeling for High-Fidelity Text-to-Motion Generation
• Authors: Xingzu Zhan, Chen Xie, Haoran Sun, Xiaochun Mai,
• Abstract summary: We propose a novel HiSTF Mamba framework for text-to-motion generation.<n>We show that HiSTF Mamba achieves state-of-the-art performance across multiple metrics.<n>These findings validate the effectiveness of HiSTF Mamba in achieving high fidelity and strong semantic alignment.
• Score: 11.63340847947103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-motion generation is a rapidly growing field at the nexus of multimodal learning and computer graphics, promising flexible and cost-effective applications in gaming, animation, robotics, and virtual reality. Existing approaches often rely on simple spatiotemporal stacking, which introduces feature redundancy, while subtle joint-level details remain overlooked from a spatial perspective. To this end, we propose a novel HiSTF Mamba framework. The framework is composed of three key modules: Dual-Spatial Mamba, Bi-Temporal Mamba, and Dynamic Spatiotemporal Fusion Module (DSFM). Dual-Spatial Mamba incorporates ``Part-based + Whole-based'' parallel modeling to represent both whole-body coordination and fine-grained joint dynamics. Bi-Temporal Mamba adopts a bidirectional scanning strategy, effectively encoding short-term motion details and long-term dependencies. DSFM further performs redundancy removal and extraction of complementary information for temporal features, then fuses them with spatial features, yielding an expressive spatio-temporal representation. Experimental results on the HumanML3D dataset demonstrate that HiSTF Mamba achieves state-of-the-art performance across multiple metrics. In particular, it reduces the FID score from 0.283 to 0.189, a relative decrease of nearly 30%. These findings validate the effectiveness of HiSTF Mamba in achieving high fidelity and strong semantic alignment in text-to-motion generation.

Related papers

• VideoMolmo: Spatio-Temporal Grounding Meets Pointing [66.19964563104385]
  VideoMolmo is a model tailored for fine-grained pointing of video sequences.<n>A novel temporal mask fusion employs SAM2 for bidirectional point propagation.<n>To evaluate the generalization of VideoMolmo, we introduce VPoMolS-temporal, a challenging out-of-distribution benchmark spanning five real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-05T17:59:29Z)
• TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba [20.941775037488863]
  Diffusion models currently demonstrate impressive performance over various generative tasks. Recent work on image diffusion highlights the strong capabilities of Mamba (state space models) We propose a novel diffusion framework containing dual latent Mamba block (DM-Block) and a time-variant frequency encoder (TF-Encoder)
  arXiv  Detail & Related papers  (2025-03-17T10:00:14Z)
• MS-Temba : Multi-Scale Temporal Mamba for Efficient Temporal Action Detection [11.534493974662304]
  Temporal Action Detection (TAD) in untrimmed videos requires models that can efficiently process long-duration videos.<n>We propose Multi-Scale Temporal Mamba (MS-Temba), the first Mamba-based architecture specifically designed for densely labeled TAD tasks.<n>MS-Temba achieves state-of-the-art performance on long-duration videos, remains competitive on shorter segments, and reduces model complexity by 88%.
  arXiv  Detail & Related papers  (2025-01-10T17:52:47Z)
• Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging [40.80197280147993]
  We propose a Mamba-inspired Joint Unfolding Network (MiJUN) to overcome the inherent nonlinear and ill-posed characteristics of HSI reconstruction.<n>We introduce an accelerated unfolding network scheme, which reduces the reliance on initial optimization stages.<n>We refine the scanning strategy with Mamba by integrating the tensor mode-$k$ unfolding into the Mamba network.
  arXiv  Detail & Related papers  (2025-01-02T13:56:23Z)
• STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detection [48.997518615379995]
  Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems.<n>Most existing methods based on CNNs and transformers still suffer from substantial computational burdens.<n>We propose a lightweight and effective Mamba-based network named STNMamba to enhance the learning of spatial-temporal normality.
  arXiv  Detail & Related papers  (2024-12-28T08:49:23Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision.<n>In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• DiM-Gestor: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 [6.6954598568836925]
  DiM-Gestor is an end-to-end generative model leveraging the Mamba-2 architecture. A fuzzy feature extractor and a speech-to-gesture mapping module are built on the Mamba-2. Our approach delivers competitive results and significantly reduces memory usage, approximately 2.4 times, and enhances inference speeds by 2 to 4 times.
  arXiv  Detail & Related papers  (2024-11-23T08:02:03Z)
• DepMamba: Progressive Fusion Mamba for Multimodal Depression Detection [37.701518424351505]
  Depression is a common mental disorder that affects millions of people worldwide. We propose an audio-visual progressive fusion Mamba for multimodal depression detection, termed DepMamba.
  arXiv  Detail & Related papers  (2024-09-24T09:58:07Z)
• PhysMamba: Efficient Remote Physiological Measurement with SlowFast Temporal Difference Mamba [20.435381963248787]
  Previous deep learning based r measurement are primarily based on CNNs and Transformers. We propose PhysMamba, a Mamba-based framework, to efficiently represent long-range physiological dependencies from facial videos. Extensive experiments are conducted on three benchmark datasets to demonstrate the superiority and efficiency of PhysMamba.
  arXiv  Detail & Related papers  (2024-09-18T14:48:50Z)
• SIGMA: Selective Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction.<n>We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation.<n>Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking [51.28485682954006]
  We propose a pure Mamba-based framework (MambaVT) to fully exploit intrinsic-temporal contextual modeling for robust visible-thermal tracking. Specifically, we devise the long-range cross-frame integration component to globally adapt to target appearance variations. Experiments show the significant potential of vision Mamba for RGB-T tracking, with MambaVT achieving state-of-the-art performance on four mainstream benchmarks.
  arXiv  Detail & Related papers  (2024-08-15T02:29:00Z)
• DiM-Gesture: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 framework [2.187990941788468]
  generative model crafted to create highly personalized 3D full-body gestures solely from raw speech audio. Model integrates a Mamba-based fuzzy feature extractor with a non-autoregressive Adaptive Layer Normalization (AdaLN) Mamba-2 diffusion architecture.
  arXiv  Detail & Related papers  (2024-08-01T08:22:47Z)
• Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs.<n>Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction.<n>We benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models [14.024240637175216]
  We propose a novel point cloud video understanding backbone based on the State Space Models (SSMs)<n> Specifically, we first disentangle space and time in 4D video sequences and then establish the spatial-temporal correlation with our designed Mamba blocks.<n>Our method has a significant efficiency improvement with 87.5% GPU memory reduction and 5.36 times speed-up.
  arXiv  Detail & Related papers  (2024-05-23T09:08:09Z)
• Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing [54.168567276280505]
  We propose a novel Mutual Information-driven Triple interaction Network (MITNet) for image dehazing. The first stage, named amplitude-guided haze removal, aims to recover the amplitude spectrum of the hazy images for haze removal. The second stage, named phase-guided structure refined, devotes to learning the transformation and refinement of the phase spectrum.
  arXiv  Detail & Related papers  (2023-08-14T08:23:58Z)
• Gait Recognition in the Wild with Multi-hop Temporal Switch [81.35245014397759]
  gait recognition in the wild is a more practical problem that has attracted the attention of the community of multimedia and computer vision. This paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes.
  arXiv  Detail & Related papers  (2022-09-01T10:46:09Z)
• Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding [61.57847727651068]
  Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query. Most previous works focus on learning frame-level features of each whole frame in the entire video, and directly match them with the textual information. We propose a novel Motion- and Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates optical-flow-guided motion-aware, detection-based appearance-aware, and 3D-aware object-level features.
  arXiv  Detail & Related papers  (2022-03-06T13:57:09Z)
• Slow-Fast Visual Tempo Learning for Video-based Action Recognition [78.3820439082979]
  Action visual tempo characterizes the dynamics and the temporal scale of an action. Previous methods capture the visual tempo either by sampling raw videos with multiple rates, or by hierarchically sampling backbone features. We propose a Temporal Correlation Module (TCM) to extract action visual tempo from low-level backbone features at single-layer remarkably.
  arXiv  Detail & Related papers  (2022-02-24T14:20:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.