Preference Learning for Enhancing Text-to-Motion Generation

Core Concepts

Preference learning can significantly improve the alignment of text-to-motion generation models with human preferences, without requiring expert-labeled motion capture data.

Abstract

This paper explores the use of preference learning to enhance text-to-motion generation models. The authors find that current text-to-motion generation methods rely on limited datasets that require expert labelers and motion capture systems, leading to poor alignment between the generated motions and the input text prompts.

To address this, the authors propose leveraging preference learning, where non-expert labelers simply compare two generated motions and provide feedback on their preferences. This approach is more cost-effective and scalable than gathering expert-labeled motion data.

The authors annotate a dataset of 3,528 preference pairs generated by the MotionGPT model and investigate various algorithms for learning from this preference data, including Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO).

The results show that models trained with preference data, particularly using the DPO approach, significantly outperform the original MotionGPT baseline in terms of alignment metrics, while maintaining comparable quality. Human evaluation also confirms that the outputs from the preference-trained models are preferred over the original MotionGPT generations.

The authors further analyze the impact of the quantity and quality of the preference data, finding that samples with a higher degree of preference provide the most significant performance gains. They also highlight the importance of proper regularization techniques, such as the use of LoRA, in the success of the DPO approach.

Overall, this work demonstrates the potential of preference learning to enhance text-to-motion generation models and paves the way for further research in this direction, particularly in the context of limited data resources.

Stats

The dataset contains 3,528 annotated preference pairs, with 996 pairs labeled as "Much better", 607 pairs labeled as "Better", 497 pairs labeled as "Slightly better", and 116 pairs labeled as "Negligibly better/unsure". Additionally, there are 1,312 examples labeled as "Skipped".

Quotes

"Learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares two generated motions."
"Our results show that labelers exhibit a significant preference for outputs from MotionGPT when trained with preference data, a trend that persists across temperatures ranging from 1.0 to 2.0."
"Our findings indicate that the scarcity of large-scale text-motion pairs leads to a propensity for the reward model to overfit. Consequently, this overfitting hampers its ability to accurately assess outputs generated by MotionGPT."

Key Insights Distilled From

Exploring Text-to-Motion Generation with Human Preference

by Jenny Sheng,... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09445.pdf

Deeper Inquiries

How can the preference learning approach be extended to other domains beyond text-to-motion generation, where data is also limited?

Preference learning can be extended to other domains with limited data by following a similar approach of collecting human preference data to train models. In domains such as image generation, music composition, or even natural language processing, where data scarcity is a challenge, preference learning can be a valuable tool. By having human labelers compare and provide feedback on generated outputs, models can be trained to align better with human preferences. This approach can help improve the quality and alignment of generated outputs in various tasks, even with limited training data.

What are the potential drawbacks or limitations of the preference learning approach, and how can they be addressed?

One potential drawback of preference learning is the risk of overfitting to the preferences provided by human labelers. This can lead to models optimizing for the specific preferences in the training data rather than generalizing well to unseen data. To address this, regularization techniques can be employed to prevent overfitting, such as adding noise to the training process or using techniques like Identity Preference Optimization (IPO) to alleviate overfitting due to the Bradley-Terry model. Additionally, ensuring a diverse and representative set of preferences in the training data can help mitigate the risk of overfitting.

How can the preference data collection process be further improved to capture more nuanced and reliable human feedback?

To capture more nuanced and reliable human feedback in the preference data collection process, several improvements can be made:

Diverse Labelers: Engage a diverse group of labelers with varying backgrounds and perspectives to provide a broader range of preferences.
Clear Guidelines: Provide clear guidelines and examples to labelers to ensure consistency and accuracy in their feedback.
Degree of Preference: Include a degree of preference scale, as done in the study, to capture the intensity of preference for each choice.
Feedback Loop: Implement a feedback loop where labelers can review and revise their choices based on feedback from other labelers or experts.
Quality Control: Implement quality control measures to ensure the reliability of the collected preference data, such as cross-validation or expert review of a subset of the data.
Iterative Process: Make the data collection process iterative, allowing for continuous improvement based on the feedback received from labelers and model performance.