Efficient Spatial-Temporal Modeling for Text-Driven Human Motion Generation

Основні поняття

A novel spatial-temporal modeling framework for generating human motions from textual prompts, which quantizes each joint into a 2D token map and leverages 2D operations to effectively capture the spatial-temporal relationships.

Анотація

The paper proposes a novel spatial-temporal modeling framework for text-driven human motion generation. The key ideas are:

1. Motion Quantization:

   • Instead of quantizing the entire body pose into one vector, the method quantizes each individual joint into a separate vector.
   • This simplifies the quantization process, maintains the spatial relationships between joints, and enables the use of 2D operations.
   • A 2D joint VQ-VAE is employed to encode the motion sequence into a 2D token map.

2. Motion Generation:

   • A temporal-spatial 2D masking strategy is proposed to randomly mask the 2D token map.
   • A spatial-temporal 2D transformer is designed to predict the masked tokens, considering both spatial and temporal attention.
   • The 2D position encoding is used to provide spatial and temporal location information for the 2D tokens.

The proposed method significantly outperforms previous state-of-the-art methods in both motion quantization and motion generation, achieving a 26.6% decrease in FID on the HumanML3D dataset and a 29.9% decrease on the KIT-ML dataset.

Статистика

"Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors."
"Previous methods usually quantize the entire body pose into one code, which not only faces the difficulty in encoding all joints within one vector but also loses the spatial relationship between different joints."
"Differently, in this work we quantize each individual joint into one vector, which i) simplifies the quantization process as the complexity associated with a single joint is markedly lower than that of the entire pose; ii) maintains a spatial-temporal structure that preserves both the spatial relationships among joints and the temporal movement patterns; iii) yields a 2D token map, which enables the application of various 2D operations widely used in 2D images."

Цитати

"Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors."
"Previous methods usually quantize the entire body pose into one code, which not only faces the difficulty in encoding all joints within one vector but also loses the spatial relationship between different joints."
"Differently, in this work we quantize each individual joint into one vector, which i) simplifies the quantization process as the complexity associated with a single joint is markedly lower than that of the entire pose; ii) maintains a spatial-temporal structure that preserves both the spatial relationships among joints and the temporal movement patterns; iii) yields a 2D token map, which enables the application of various 2D operations widely used in 2D images."

Ключові висновки, отримані з

MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling

by Weihao Yuan,... о arxiv.org 09-27-2024

https://arxiv.org/pdf/2409.17686.pdf

Глибші Запити

How can the proposed spatial-temporal modeling framework be extended to handle more complex motion patterns, such as interactions between multiple people or the incorporation of physical constraints?

The proposed spatial-temporal modeling framework can be extended to handle more complex motion patterns by integrating multi-agent interaction modeling and incorporating physical constraints into the motion generation process.

Multi-Agent Interaction Modeling: To effectively model interactions between multiple people, the framework could be adapted to include a multi-agent system where each agent (or person) is represented by its own spatial-temporal token map. This would involve creating a joint representation that captures the spatial relationships not only within an individual but also between different individuals. Techniques such as graph neural networks (GNNs) could be employed to model the interactions dynamically, allowing the framework to learn how the movements of one agent influence another. This would enhance the realism of generated motions in scenarios like group dances, sports, or conversations.

Incorporation of Physical Constraints: To ensure that the generated motions adhere to realistic physical behaviors, the framework could integrate physics-based constraints. This could involve using a physics engine to simulate the effects of gravity, collision detection, and joint limits during the motion generation process. By embedding these constraints into the training phase, the model could learn to generate motions that are not only contextually appropriate but also physically plausible. Additionally, reinforcement learning techniques could be applied to refine the generated motions based on feedback from a physics simulation, further enhancing the quality and realism of the output.

Hierarchical Motion Representation: Another approach could involve a hierarchical representation of motion, where high-level actions are defined and then decomposed into lower-level joint movements. This would allow the model to generate complex sequences of interactions by first determining the overarching actions (e.g., "two people shaking hands") and then detailing the specific joint movements required to achieve those actions.

What are the potential limitations of the 2D token representation, and how could it be further improved to capture more nuanced spatial-temporal relationships in human motion?

While the 2D token representation offers several advantages, such as simplifying the quantization process and enabling the use of 2D operations, it also has potential limitations:

Loss of Depth Information: The 2D representation inherently flattens the spatial relationships, which may lead to a loss of depth information crucial for accurately modeling human motion in three-dimensional space. This could result in unrealistic joint configurations or movements that do not account for the three-dimensional nature of human anatomy.

Limited Contextual Awareness: The current framework may struggle to capture long-range dependencies and complex interactions between joints over time. While the spatial-temporal attention mechanisms help, they may not fully exploit the intricate relationships that exist in human motion, especially in scenarios involving rapid changes or complex sequences.

Improvement Strategies: To address these limitations, the framework could be enhanced by:

3D Token Representation: Transitioning from a 2D to a 3D token representation could allow for a more accurate modeling of human motion, preserving depth and spatial relationships more effectively. This would involve extending the current framework to handle three-dimensional data, potentially using volumetric representations or 3D convolutional networks.
Temporal Hierarchies: Implementing a hierarchical temporal structure could help capture long-range dependencies in motion. By segmenting motion sequences into phases or actions, the model could better understand the context and dynamics of movements over time.
Attention Mechanisms: Further refining the attention mechanisms to include multi-scale attention could allow the model to focus on both local and global features of the motion, enhancing its ability to generate nuanced and contextually appropriate movements.

Given the success of the proposed method in text-driven motion generation, how could it be adapted to other modalities, such as generating motions from audio or video inputs?

The proposed method can be adapted to generate motions from other modalities, such as audio or video inputs, by leveraging the inherent characteristics of these data types and modifying the framework accordingly:

Audio-Driven Motion Generation: To generate motions from audio inputs, the framework could incorporate an audio feature extraction module that processes audio signals to extract relevant features, such as rhythm, pitch, and intensity. These features could then be mapped to specific motion characteristics, allowing the model to generate movements that correspond to the emotional tone or rhythm of the audio. For instance, upbeat music could trigger more dynamic and energetic movements, while softer sounds might result in smoother, more fluid motions. Techniques such as recurrent neural networks (RNNs) or transformers could be employed to capture the temporal dynamics of audio signals effectively.

Video-Based Motion Generation: For generating motions from video inputs, the framework could utilize a video analysis module that extracts motion features from the video frames. This could involve using optical flow techniques to capture the movement of joints across frames or employing convolutional neural networks (CNNs) to identify and track key poses. The extracted features could then be integrated into the existing spatial-temporal modeling framework, allowing the model to generate new motions based on the observed behaviors in the video. Additionally, the framework could be trained on paired video and motion data to learn the mapping between visual cues and corresponding joint movements.

Cross-Modal Learning: Implementing a cross-modal learning approach could enhance the model's ability to generalize across different input types. By training the model on a diverse dataset that includes text, audio, and video inputs, it could learn to generate motions that are coherent and contextually relevant, regardless of the modality. This would involve designing a unified representation that captures the essential features from each modality and allows for seamless integration during the motion generation process.

By adapting the framework to accommodate these modalities, the potential applications of the motion generation model could be significantly expanded, enabling more versatile and interactive systems in fields such as gaming, virtual reality, and human-computer interaction.