The paper presents a method for generating lower‑limb joint‑angle gait trajectories using conditional diffusion models. It compares a baseline transformer diffusion model with a controllable diffusion transformer that includes adaptive normalization and classifier‑free guidance. Experiments on 4,590 gait cycles demonstrate that these diffusion models can produce realistic, periodic gait patterns while allowing some control over gait characteristics such as step length.
arXiv:2609.18490v1 Announce Type: new Abstract: "What I cannot create, I do not understand."Human wisdom reveals that creation is one of the highest forms of learning. For example, Diffusion Models h...
arXiv:2606. 27918v1 Announce Type: cross Abstract: As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and particularly, the number of steps required to complete a turn, which directly reflects motor dysfunction.
arXiv:2512. 00691v2 Announce Type: replace Abstract: Gait patterns play a critical role in human identification and healthcare analytics, yet current progress remains constrained by small, narrowly designed models that fail to scale or generalize.
arXiv:2512. 08280v3 Announce Type: replace-cross Abstract: Offline decision-making via diffusion models often produces trajectories that are misaligned with system dynamics, limiting their reliability for control.
arXiv:2603. 12408v2 Announce Type: replace-cross Abstract: Motion imitation learning (IL) is increasingly used in robotics and human gait modeling, yet its ability to recover biomechanically consistent joint moments without explicit kinetic information remains unclear.
arXiv:2408. 08182v5 Announce Type: replace-cross Abstract: People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disease progresses.
arXiv:2606. 00153v1 Announce Type: cross Abstract: Cross-modal 2D-3D gait recognition is impeded by inherent domain discrepancies between 2D silhouette and 3D LiDAR range-view representations.
arXiv:2603. 12408v3 Announce Type: replace-cross Abstract: Motion imitation learning (IL) is increasingly used in robotics and human gait modeling, yet its ability to recover biomechanically consistent joint moments without explicit kinetic information remains unclear.
arXiv:2609.09670v1 Announce Type: new Abstract: Monocular pose estimation enables low-cost gait analysis but is sensitive to missing keypoints caused by occlusion, detection errors, or efficiency-dri...
arXiv:2512. 10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities constructions that reweight time-indexed marginals with fixed exponents.
arXiv:2607. 12114v1 Announce Type: cross Abstract: A humanoid that can walk should not relearn locomotion from scratch to jog or run.
RevalExo is a new benchmark for locomotion mode recognition that focuses on functional daily activities performed by older adults and clinical cohorts. It includes 27 participants from three groups—healthy older adults, stroke survivors, and older adults with probable sarcopenia—recorded with lower-body IMUs and, for a subset, synchronized egocentric video. The dataset offers 10.1 hours of frame‑level annotations across 11 locomotion modes, and the authors evaluate unimodal, multimodal, cross‑population, and cross‑modal recognition challenges, finding that sensor fusion improves performance but transitions and generalization remain difficult.