Artificial Intelligence is revolutionizing various industries, including animation, video games, and film. However, Story-to-Motion, the task of translating written descriptions into natural human movement for characters, poses challenges. Existing approaches have limitations, but researchers have introduced a new method that effectively generates controlled and realistic motions based on input text. The approach includes text-driven motion scheduling, a motion retrieval system, and a progressive mask transformer. The evaluation shows improved performance in motion blending, temporal action composition, and trajectory following. Overall, this research offers significant advancements in generating human motions from textual narratives, with potential game-changing impacts in animation, gaming, and film.
Introducing Story-to-Motion: An AI Approach to Generate Human Motion and Trajectory from Text
Artificial Intelligence is revolutionizing various industries, including animation, video games, and film. One of the most challenging tasks in these industries is converting a story into lifelike human movement. Traditional methods have limitations in handling textual descriptions, leading to unstable motions. However, a team of researchers has developed a groundbreaking solution.
Key Components of the Approach
The proposed approach consists of three primary components:
1. Text-Driven Motion Scheduling
Modern Large Language Models use long textual descriptions to generate motions. This stage ensures that the generated motions align with the story and include details about location and duration.
2. Text-Driven Motion Retrieval System
This system combines motion matching and constraints to generate motions that adhere to the intended semantics and positional properties mentioned in the text description.
3. Progressive Mask Transformer
This component addresses common issues in transition motions, such as foot sliding and unusual stances. It enhances the quality of generated motions, resulting in smoother transitions and a more realistic appearance.
Benefits and Results
The approach has been tested and evaluated on motion blending, temporal action composition, and trajectory following tasks. It has shown improved performance compared to previous methods. The primary contributions of this research include:
- Generating comprehensive motion from lengthy textual descriptions, solving the Story-to-Motion problem.
- Introducing Text-based Motion Matching for accurate and customizable motion synthesis using extensive text input.
- Outperforming state-of-the-art techniques in trajectory following, temporal action composition, and motion blending.
Implications and Future Prospects
This AI system is a significant advancement in generating human motions from text narratives. It provides practical solutions to the challenges of Story-to-Motion tasks. The system is expected to have a game-changing impact on the animation, gaming, and film industries.
For more details, read the full paper and explore the project.
Applying AI in Your Company
If you want to leverage AI to stay competitive and transform your company, consider adopting the Story-to-Motion approach. It’s crucial to follow these steps:
- Identify Automation Opportunities: Find key customer interaction points that can benefit from AI.
- Define KPIs: Ensure that your AI initiatives have measurable impacts on business outcomes.
- Select an AI Solution: Choose AI tools that align with your needs and offer customization options.
- Implement Gradually: Start with a pilot project, gather data, and expand AI usage thoughtfully.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- SenseTime Research Propose Story-to-Motion: A New Artificial Intelligence Approach to Generate Human Motion and Trajectory from a Long Text
- MarkTechPost
- Twitter – @itinaicom
