The field of human motion generation is currently moving towards more realistic and controllable models, with a focus on incorporating attributes such as age, gender, and environment into the generation process. Researchers are exploring new frameworks and methods to decouple action semantics from human attributes, enabling more precise control over the generated motions. Another significant direction is the development of models that can generate human-object interactions, taking into account the dynamics of the interaction and the affordance of the environment. Real-time inverse kinematics solvers are also being developed to generate accurate and realistic virtual human movements. Furthermore, there is a growing interest in using transformer-based architectures and pretraining methods to improve the efficiency and effectiveness of human motion generation models. Notable papers include:

• A paper that proposes a novel framework for attribute-aware human motion generation, which enables the control of human attributes such as age and gender.
• A paper that presents a real-time inverse kinematics solver for generating multi-constrained movements of virtual human characters, which outperforms existing methods in terms of coherence and stability.
• A paper that introduces a lightweight transformer-based interaction dynamics model for generating realistic 3D human-object interactions.