Current Trends in Human Motion Simulation and Generation

Recent advancements in human motion simulation and generation have significantly enhanced the accuracy and realism of digital human models, particularly in the context of human-robot interaction and digital human applications. The field is witnessing a shift towards more personalized and physically plausible models, driven by the integration of soft-body dynamics and advanced control policies. Innovations in motion prediction and generation are leveraging wavelet transformations and frequency-domain analysis to capture subtle temporal and spatial nuances, leading to more accurate and generalized models.

The incorporation of physics optimization frameworks is also notable, as these methods refine synthetic motion data to enforce physical constraints, thereby improving the plausibility of generated motions. Additionally, the development of diffusion models that incorporate frequency and text state space models is advancing the consistency between textual descriptions and generated motions, enhancing the semantic alignment in text-to-motion tasks.

Noteworthy contributions include the creation of personalized 3D digital twins with soft-body feet for more accurate human-robot interaction simulations, the introduction of a motion-free physics optimization framework for generating physically plausible human motions, and the development of a wavelet-based motion prediction framework that enhances temporal and spatial accuracy. These innovations collectively push the boundaries of what is possible in human motion simulation and generation, paving the way for more sophisticated and realistic applications in digital humans and humanoid robotics.

Noteworthy Papers

• Personalised 3D Human Digital Twin with Soft-Body Feet for Walking Simulation: Introduces soft-body feet for more accurate ground reaction force and joint angle results in human-robot interaction simulations.
• Morph: A Motion-free Physics Optimization Framework for Human Motion Generation: Enhances physical plausibility in motion generation without relying on real-world motion data, achieving state-of-the-art results in text-to-motion and music-to-dance tasks.
• MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning: Utilizes wavelet transformation for capturing intricate motion patterns, improving prediction accuracy and generalization across benchmarks.
• FTMoMamba: Motion Generation with Frequency and Text State Space Models: Combines frequency and text state space models to generate fine-grained and semantically consistent human motions, achieving superior performance in text-to-motion tasks.