Text-to-Motion Generation Using Discriminator-Guided Reinforcement Learning
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Solution Overview
Problem
Existing generative models struggle to generate realistic and accurate motion from text inputs due to inconsistencies in human perception and the inefficiency of current training methods, which are often expensive and time-consuming.
Innovation Solution
A generative model is trained using a discriminator to align generated motion with text input through reinforcement learning, utilizing a contrastive model to measure embedding differences and adjust network weights based on alignment scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human feedback is incorporated in the training process to improve motion generation accuracy, then the quality of generated motion is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent introduces a discriminator model as an intermediary between the generative model and human feedback. The discriminator automatically evaluates whether generated motion matches the text input, replacing the need for direct human evaluation in each training iteration. This intermediary system provides consistent, automated feedback while preserving the alignment between generated motion and text descriptions.
Solution Approach 2:
The patent implements a feedback loop where the discriminator's evaluation results are used to adjust the training process. The discriminator provides continuous feedback on the alignment between generated motion and text input, enabling the generative model to iteratively improve its performance without requiring repeated human intervention.
2Manufacturing precision
If the training dataset is improved by increasing the total amount of training examples to enhance model performance, then the quality of generated motion is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The discriminator acts as an intermediary that enables effective training with smaller datasets. By providing automated evaluation and alignment assessment, the discriminator allows the model to learn more efficiently from limited examples, reducing the need for large-scale data collection and annotation.
Solution Approach 2:
The patent replaces the mechanical process of manually collecting and annotating large amounts of training data with an automated system. The discriminator automatically evaluates generated motion against text inputs, substituting the need for extensive manual data preparation and enabling training with smaller, more manageable datasets.
3Productivity
If existing generative models are used to generate motion from text input, then the process is simple and fast, but the generated motion is not always correct or realistic
Solution Approach 1:
The patent introduces feedback through the discriminator that continuously evaluates whether generated motion accurately reflects the text input. This feedback mechanism allows the system to maintain fast generation speeds while improving accuracy through iterative refinement guided by the discriminator's assessment.
Solution Approach 2:
The discriminator serves as an intermediary quality control layer between the text input and generated motion. It automatically verifies alignment and realism without significantly slowing down the generation process, enabling the system to produce accurate and realistic motion while maintaining computational efficiency.
Data Source
AI summary
Approaches presented herein provide for the use of reinforcement learning to fine-tune a generative model, such as a motion diffusion model, for a specific objective, such as to generate representations of human motion corresponding to provided text input. A discriminator can be used to guide the training of the generative model. In at least one embodiment, the discriminator can compare the input text and generated motion representation (or embeddings of each) to determine an alignment value or match score, for example, which can then be used to adjust the network parameters or weights of the generative model to improve the alignment between input text and generated motion.