インサイト - Natural Language Processing - # Text-to-Motion Generation from Arbitrary Texts

Generating Diverse Human Motions from Arbitrary Texts: A Practical Approach to Text2Motion

Q: How can the proposed framework be extended to handle more complex and diverse scene texts, such as those involving multiple agents or complex interactions

To handle more complex and diverse scene texts, such as those involving multiple agents or complex interactions, the proposed framework can be extended in the following ways: Multi-Agent Interaction Modeling: Incorporate mechanisms to model interactions between multiple agents in the scene. This can involve developing attention mechanisms or graph neural networks to capture dependencies and interactions between different agents. Hierarchical Text Understanding: Implement a hierarchical text understanding approach to parse and comprehend complex scene descriptions. This can involve breaking down the scene text into sub-components and generating actions for each component before integrating them into a cohesive motion sequence. Contextual Information Integration: Integrate contextual information from the scene text to enhance the understanding of relationships and dependencies between different elements in the scene. This can help in generating more coherent and contextually relevant motion sequences.

Q: What are the potential limitations of the current LLM-based approach in extracting action labels from scene texts, and how can these limitations be addressed

The current LLM-based approach may have limitations in extracting action labels from scene texts, such as: Ambiguity in Scene Texts: Scene texts may contain ambiguous or vague descriptions that make it challenging for LLMs to accurately extract action labels. This ambiguity can lead to incorrect or incomplete understanding of the scene. Limited Contextual Understanding: LLMs may struggle to capture the full context of the scene text, especially in cases where implicit information or background knowledge is required to infer the correct action labels. Overfitting to Training Data: LLMs may overfit to the training data, leading to biases or limited generalization to unseen scene texts. These limitations can be addressed by: Fine-tuning LLMs: Fine-tuning the LLM on a diverse set of scene texts can help improve its ability to extract action labels accurately. Ensemble Models: Using ensemble models or combining LLMs with other architectures like Transformers can enhance the contextual understanding and reduce ambiguity in action label extraction. Data Augmentation: Augmenting the training data with a variety of scene texts can help the LLM learn to handle diverse and complex scenarios better.

Q: How can the generated motions be further integrated into interactive applications, such as embodied intelligence or open-world games, to enhance the user experience

To integrate the generated motions into interactive applications like embodied intelligence or open-world games, the following strategies can be employed: Real-time Motion Generation: Implement a real-time motion generation system that can dynamically generate motions based on user interactions or environmental stimuli. This can enhance the responsiveness and adaptability of the motions in interactive scenarios. User Feedback Integration: Incorporate user feedback mechanisms to refine the generated motions based on user preferences or interactions. This can personalize the motions and enhance user engagement. Interactive Motion Control: Develop interfaces or tools that allow users to interactively control or modify the generated motions in real-time. This can empower users to create custom animations or behaviors within the application. Scenario-based Motion Generation: Tailor the motion generation process to specific scenarios or tasks within the application, ensuring that the generated motions align with the intended context and user experience. Multi-modal Integration: Integrate the generated motions with other modalities like speech or visual cues to create a more immersive and interactive experience for users. This can enhance the overall user experience and engagement in the application.

核心概念

This paper proposes a novel two-stage framework to generate human motions from arbitrary texts, including both action texts and scene texts, by leveraging the strengths of large language models and transformer-based motion generation.

要約

The paper introduces a new dataset, HumanML3D++, which expands the existing HumanML3D dataset by adding scene texts to the action texts. This dataset enables the exploration of generating motions from arbitrary texts, going beyond the previous focus on action texts.

The proposed framework consists of two main components:

Think Model: This module uses a large language model (LLM) to extract action labels from the given arbitrary texts, handling both action texts and scene texts.
Act Model: This module employs a transformer-based generative model to generate the final motion sequences from the extracted action labels.

The authors conduct extensive experiments to evaluate the performance of their framework and compare it with existing state-of-the-art methods. The results demonstrate that the proposed two-stage approach can effectively generate high-quality and diverse human motions from arbitrary texts, outperforming previous methods that were limited to action texts.

The key highlights of the paper include:

Introducing the HumanML3D++ dataset with scene text annotations to enable the study of generating motions from arbitrary texts.
Proposing a novel two-stage framework that leverages the strengths of LLMs and transformer-based motion generation.
Demonstrating the effectiveness of the proposed approach in generating diverse and realistic human motions from arbitrary texts, including both action texts and scene texts.
Providing insights into the challenges and opportunities in the practical application of text-to-motion generation.

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arxiv.org

統計

"A person notices his wallet on the ground ahead"
"A person takes a few steps forward and then bends down to pick up something"

引用

"Exploring the generation of potential motions from arbitrary texts is important."
"Compared to them, it is more practical to generate motions from arbitrary texts (the right figure in Figure 1), such as 'A person notices his wallet on the ground ahead'."

抽出されたキーインサイト

TAAT: Think and Act from Arbitrary Texts in Text2Motion

by Runqi Wang,C... 場所 arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14745.pdf

TAAT: Think and Act from Arbitrary Texts in Text2Motion

深掘り質問

How can the proposed framework be extended to handle more complex and diverse scene texts, such as those involving multiple agents or complex interactions

To handle more complex and diverse scene texts, such as those involving multiple agents or complex interactions, the proposed framework can be extended in the following ways:

Multi-Agent Interaction Modeling: Incorporate mechanisms to model interactions between multiple agents in the scene. This can involve developing attention mechanisms or graph neural networks to capture dependencies and interactions between different agents.
Hierarchical Text Understanding: Implement a hierarchical text understanding approach to parse and comprehend complex scene descriptions. This can involve breaking down the scene text into sub-components and generating actions for each component before integrating them into a cohesive motion sequence.
Contextual Information Integration: Integrate contextual information from the scene text to enhance the understanding of relationships and dependencies between different elements in the scene. This can help in generating more coherent and contextually relevant motion sequences.

What are the potential limitations of the current LLM-based approach in extracting action labels from scene texts, and how can these limitations be addressed

The current LLM-based approach may have limitations in extracting action labels from scene texts, such as:

Ambiguity in Scene Texts: Scene texts may contain ambiguous or vague descriptions that make it challenging for LLMs to accurately extract action labels. This ambiguity can lead to incorrect or incomplete understanding of the scene.
Limited Contextual Understanding: LLMs may struggle to capture the full context of the scene text, especially in cases where implicit information or background knowledge is required to infer the correct action labels.
Overfitting to Training Data: LLMs may overfit to the training data, leading to biases or limited generalization to unseen scene texts.

These limitations can be addressed by:

Fine-tuning LLMs: Fine-tuning the LLM on a diverse set of scene texts can help improve its ability to extract action labels accurately.
Ensemble Models: Using ensemble models or combining LLMs with other architectures like Transformers can enhance the contextual understanding and reduce ambiguity in action label extraction.
Data Augmentation: Augmenting the training data with a variety of scene texts can help the LLM learn to handle diverse and complex scenarios better.

How can the generated motions be further integrated into interactive applications, such as embodied intelligence or open-world games, to enhance the user experience

To integrate the generated motions into interactive applications like embodied intelligence or open-world games, the following strategies can be employed:

Real-time Motion Generation: Implement a real-time motion generation system that can dynamically generate motions based on user interactions or environmental stimuli. This can enhance the responsiveness and adaptability of the motions in interactive scenarios.
User Feedback Integration: Incorporate user feedback mechanisms to refine the generated motions based on user preferences or interactions. This can personalize the motions and enhance user engagement.
Interactive Motion Control: Develop interfaces or tools that allow users to interactively control or modify the generated motions in real-time. This can empower users to create custom animations or behaviors within the application.
Scenario-based Motion Generation: Tailor the motion generation process to specific scenarios or tasks within the application, ensuring that the generated motions align with the intended context and user experience.
Multi-modal Integration: Integrate the generated motions with other modalities like speech or visual cues to create a more immersive and interactive experience for users. This can enhance the overall user experience and engagement in the application.