Motion Generation and Imitation Learning

The field of motion generation and imitation learning is rapidly advancing with a focus on developing more sophisticated and efficient methods for synthesizing realistic motion sequences. Researchers are exploring various generative approaches, including GANs, autoencoders, and diffusion-based techniques, to improve the fidelity and diversity of generated motions. A key direction in this field is the development of methods that can learn from demonstrations and adapt to new situations, with a particular emphasis on structured motion representations and task-specific priorities. The use of large-scale datasets and scalable architectures is also becoming increasingly important, enabling researchers to push the boundaries of zero-shot motion generation and long-video storytelling. Notable papers in this area include:

  • MOST, which introduces a novel motion diffusion model for generating human motion from rare language prompts, achieving state-of-the-art performance.
  • Go to Zero, which proposes a scalable architecture and introduces the largest human motion dataset to date, demonstrating strong generalization to out-of-domain and complex compositional motions.
  • Behave Your Motion, which presents a habit-preserved cross-category animal motion transfer framework, allowing for the preservation of distinct habitual behaviors in animals.

Sources

Motion Generation: A Survey of Generative Approaches and Benchmarks

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why

MOST: Motion Diffusion Model for Rare Text via Temporal Clip Banzhaf Interaction

Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement

Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data

A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality

Behave Your Motion: Habit-preserved Cross-category Animal Motion Transfer

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