Towards Zero-Shot Human Motion Generation via Fine-Grained Textual Descriptions

核心概念

Fine-grained textual descriptions of different body parts can guide a transformer-based diffusion model to generate human motions beyond the distribution of the training dataset.

要約

The paper proposes a novel framework called Fine-Grained Human Motion Diffusion Model (FG-MDM) for zero-shot human motion generation. The key ideas are:

Leveraging a large language model (ChatGPT) to convert short and vague textual descriptions into fine-grained descriptions of different body parts (e.g., "His arms sway freely by his sides. His legs move with energy, taking long strides."). This provides the model with more detailed information about the desired motion.

Incorporating these fine-grained descriptions into a transformer-based diffusion model. Specifically, the model encodes the entire fine-grained description as a global token, and the descriptions of individual body parts as part tokens. This allows the model to attend to both the overall motion and the details of each body part.

Evaluating the model's ability to generate motions beyond the distribution of the training dataset (HumanML3D and KIT). Experiments show that FG-MDM outperforms previous state-of-the-art methods in zero-shot settings, generating motions that better match the fine-grained textual descriptions.

Releasing the fine-grained textual annotations for HumanML3D and KIT datasets, which can benefit future research on text-driven human motion generation.

統計

A person walks happily. His legs move with energy, taking long strides.
A person walks depressingly. His legs move slowly, taking short steps with little energy.
His arms sway freely by his sides.
His arms hang heavily by his sides.
His neck is held high and comfortably.
His neck is lowered.

引用

"A person walks happily."
"A person walks depressingly."
"His arms sway freely by his sides."
"His arms hang heavily by his sides."
"His legs move with energy, taking long strides."
"His legs move slowly, taking short steps with little energy."
"His neck is held high and comfortably."
"His neck is lowered."

抽出されたキーインサイト

FG-MDM: Towards Zero-Shot Human Motion Generation via Fine-Grained Descriptions

by Xu Shi,Wei Y... 場所 arxiv.org 04-24-2024

https://arxiv.org/pdf/2312.02772.pdf

深掘り質問

How can the fine-grained textual descriptions be further improved to better capture the nuances of human motion?

To enhance the quality and accuracy of fine-grained textual descriptions for capturing the nuances of human motion, several strategies can be implemented:

Incorporating Kinematic Details: Include specific details about joint angles, velocities, and accelerations in the descriptions to provide a more comprehensive understanding of the motion.

Temporal Context: Describe the sequence of movements over time, highlighting transitions between poses and the flow of motion to create a more coherent narrative.

Emotional and Expressive Cues: Integrate emotional cues and expressive elements into the descriptions to convey the mood, intention, and style of the motion accurately.

Anatomical References: Use anatomical references to specify body parts, muscle groups, and skeletal movements involved in the motion to ensure precision and clarity.

Spatial Awareness: Include spatial references and positional information to depict the spatial orientation and interactions of the body parts during the motion.

Consistency and Cohesion: Ensure consistency in the descriptions across different body parts and movements to maintain coherence and avoid contradictions.

By incorporating these elements into the fine-grained textual descriptions, the model can better capture the nuances of human motion and generate more realistic and detailed animations.

How can the proposed framework be extended to generate motions for other types of characters, such as animals or fictional creatures, based on textual descriptions?

To extend the proposed framework for generating motions for different types of characters, such as animals or fictional creatures, the following adaptations can be made:

Customized Motion Libraries: Develop specialized motion libraries tailored to the movement characteristics of animals or fictional creatures, including unique gaits, behaviors, and anatomical features.

Species-Specific Descriptions: Create fine-grained textual descriptions that are specific to the anatomy and locomotion patterns of the target species, incorporating details like paw movements, wing flapping, or tail swishing.

Behavioral Cues: Integrate behavioral cues and natural instincts into the descriptions to capture the essence of how animals or creatures interact with their environment and exhibit characteristic movements.

Visual References: Utilize visual references, such as images or videos, to supplement the textual descriptions and provide a visual guide for generating accurate and lifelike motions.

Adaptation of Model Architecture: Modify the model architecture to accommodate the unique skeletal structures and movement dynamics of animals or creatures, ensuring that the generated motions are biomechanically plausible.

By customizing the framework to cater to the specific movement patterns and characteristics of different types of characters, the model can effectively generate diverse and realistic motions beyond human figures.

What other modalities, besides text, could be leveraged to guide the motion generation process and improve the model's zero-shot capabilities?

In addition to text, the motion generation process can be guided and enhanced by leveraging the following modalities:

Audio Descriptions: Incorporate audio descriptions or sound cues that convey the rhythm, pace, and intensity of the motion, providing additional sensory information to enrich the generation process.

Visual Sketches or Drawings: Use visual sketches or drawings to illustrate the desired poses, movements, and gestures, serving as a visual reference for the model to generate corresponding motions.

Motion Capture Data: Integrate motion capture data from real-world performances or animations to provide direct input for the model, enabling it to learn from authentic human movements and gestures.

Biometric Data: Utilize biometric data, such as heart rate, muscle activity, or body temperature, to capture the physiological responses and emotional states associated with different motions, adding a layer of realism to the generated animations.

Semantic Web Annotations: Employ semantic web annotations or ontologies to encode rich metadata about the motions, including contextual information, relationships between actions, and hierarchical structures, facilitating a more nuanced understanding of the motion semantics.

By incorporating these diverse modalities into the motion generation framework, the model can benefit from multi-modal inputs to improve its zero-shot capabilities and generate more contextually relevant and expressive motions.