BOTH2Hands: Generating Realistic Two-Hand Motions from Text Prompts and Body Dynamics

Основные понятия

BOTH2Hands is a novel scheme that can generate vivid two-hand motions by effectively combining explicit text prompts and implicit body dynamics.

Аннотация

The paper introduces BOTH2Hands, a novel approach for generating realistic two-hand motions from both text prompts and body dynamics.
Key highlights:

The authors propose a large-scale multi-modal dataset called BOTH57M, which contains accurate motion tracking for the human body and hands, as well as rich finger-level hand annotations and body descriptions.
BOTH2Hands uses a two-stage mechanism - first, it warms up two parallel body-to-hand and text-to-hand diffusion models. Then, it utilizes a cross-attention transformer to blend the conditioned hand motions.
The body-to-hand diffusion directly predicts the absolute positions and rotations of hand joints, while the text-to-hand diffusion uses local hand representations to focus on gestures. A projection step is used to align the two representations.
Extensive experiments and cross-validations demonstrate the effectiveness of the BOTH57M dataset and the BOTH2Hands approach for generating convincing two-hand motions from hybrid body-and-textual conditions.

Статистика

Our BOTH57M dataset contains 57.4 million frames of 8.31 hours with 23,477 textual annotations.
The standard deviation of hand joint positions in BOTH57M is 0.422, indicating rich diversity of hand motions.

Цитаты

"BOTH2Hands is the only model that can handle text prompts and body dynamics as input, generating realistic hand motions at present."
"Our dataset and code will be released to the community for future research."

Ключевые выводы из

BOTH2Hands

by Wenqian Zhan... в arxiv.org 04-10-2024

https://arxiv.org/pdf/2312.07937.pdf

Дополнительные вопросы

How can the BOTH2Hands approach be extended to handle more complex multi-modal conditions beyond just text and body dynamics?

To extend the BOTH2Hands approach to handle more complex multi-modal conditions, we can incorporate additional modalities such as audio, image, or even sensor data. By integrating these diverse sources of information, the model can gain a more comprehensive understanding of the context in which the hand motions are generated. For example, audio cues can provide insights into the tone or emotion of a conversation, while image data can offer visual context that complements the textual prompts. Sensor data, such as motion capture or environmental sensors, can further enrich the input conditions by capturing real-time physical interactions or environmental factors that influence hand movements.
By integrating these additional modalities, the BOTH2Hands framework can create a more holistic representation of the multi-modal conditions influencing hand motion generation. This expanded approach can lead to more nuanced and contextually relevant hand motions that better reflect the complexities of human communication and interaction.

What are the potential limitations of the current evaluation metrics in capturing the nuances of multi-conditioned hand motion generation?

The current evaluation metrics, such as R Precision, FID, MM-Dist, Diversity, and MModality, may have limitations in capturing the nuances of multi-conditioned hand motion generation due to several reasons:

Limited Scope: These metrics may focus more on individual aspects of motion generation, such as accuracy or diversity, and may not fully capture the interplay between different modalities in multi-conditioned scenarios.

Sensitivity to Specific Conditions: Some metrics may be more sensitive to specific conditions or modalities, leading to biased evaluations that do not reflect the overall performance of the model across diverse inputs.

Lack of Contextual Understanding: The metrics may not account for the contextual understanding required in multi-modal hand motion generation, where the interactions between text, body dynamics, and other modalities play a crucial role in shaping the generated motions.

Interpretability: The metrics may not provide insights into the interpretability of the generated hand motions in relation to the input conditions, making it challenging to assess the model's ability to capture subtle nuances and variations in gestures.

To address these limitations, new evaluation metrics that consider the holistic nature of multi-conditioned hand motion generation and the complex interactions between different modalities may be needed. These metrics should provide a more comprehensive assessment of the model's performance in capturing the nuances of human communication and interaction through hand gestures.

How can the BOTH2Hands framework be adapted to enable interactive control and real-time generation of two-hand motions?

To enable interactive control and real-time generation of two-hand motions, the BOTH2Hands framework can be adapted in the following ways:

Real-time Inference: Implement optimized inference algorithms and parallel processing techniques to reduce latency and enable real-time generation of hand motions in response to user inputs.

Interactive Interface: Develop a user-friendly interface that allows users to input text prompts, body movements, and other modalities in real-time, providing immediate feedback on the generated hand motions.

Dynamic Adjustment: Incorporate mechanisms for dynamic adjustment of generated hand motions based on user feedback or changes in input conditions, allowing for interactive control over the motion synthesis process.

Feedback Loop: Implement a feedback loop where users can provide input on the generated hand motions, enabling the model to adapt and refine its outputs in real-time based on user preferences and corrections.

By incorporating these adaptations, the BOTH2Hands framework can facilitate interactive control and real-time generation of two-hand motions, enhancing its usability in applications requiring responsive and contextually relevant hand gesture synthesis.