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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Human Motion Video Generation: A Survey
Summary
This survey provides a comprehensive overview of human motion video generation, covering key phases and modalities. It highlights the emerging role of large language models (LLMs) in advancing this dynamic field.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Human motion video generation is crucial for applications like virtual avatars and digital humans.
- Existing research surveys lack a holistic view of the entire generative process.
- This field encompasses diverse sub-tasks and generation phases.
Purpose of the Study:
- To provide an in-depth survey of human motion video generation.
- To offer a comprehensive overview of the generative process, from input to output.
- To explore the potential of large language models (LLMs) in this domain.
Main Methods:
- Reviewing over 200 research papers on human motion video generation.
- Analyzing the five key phases: input, motion planning, generation, refinement, and output.
- Examining trends across vision, text, and audio modalities.
Main Results:
- Identified over ten sub-tasks within human motion video generation.
- Detailed the complete five-phase generative pipeline.
- Highlighted the novel application of LLMs for enhancing motion generation.
Conclusions:
- Human motion video generation is a rapidly evolving field with significant potential.
- This survey serves as a valuable resource for researchers and developers.
- Future advancements will likely leverage LLMs for more sophisticated digital human applications.
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