T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations (2301.06052v4)

Abstract: In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions. We show that a simple CNN-based VQ-VAE with commonly used training recipes (EMA and Code Reset) allows us to obtain high-quality discrete representations. For GPT, we incorporate a simple corruption strategy during the training to alleviate training-testing discrepancy. Despite its simplicity, our T2M-GPT shows better performance than competitive approaches, including recent diffusion-based approaches. For example, on HumanML3D, which is currently the largest dataset, we achieve comparable performance on the consistency between text and generated motion (R-Precision), but with FID 0.116 largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach. Our work suggests that VQ-VAE still remains a competitive approach for human motion generation.

• The paper presents a two-stage framework that first encodes motion using a CNN-based VQ-VAE, then generates motion codes with a GPT model.
• Experimental results on HumanML3D and KIT-ML show superior performance with an FID of 0.116, outperforming diffusion-based methods.
• The study underscores the potential of simpler architectures and robust training strategies for efficient, text-conditioned human motion synthesis.

T2M-GPT: An Analysis of a Framework for Text-Conditioned Human Motion Generation

The authors of the paper present an advanced approach to generating human motion from textual descriptions utilizing both Vector Quantised-Variational AutoEncoders (VQ-VAE) and Generative Pre-trained Transformer (GPT). This research seeks to address challenges within the field of multimodal AI, particularly the synthesis of understandable motion representations from human language.

Framework Overview

The core of the paper introduces a two-stage framework termed T2M-GPT. In Stage 1, the method leverages a straightforward CNN-based VQ-VAE to convert motion sequences into discrete code indices, demonstrating that high-quality discrete representations can be created using conventional training recipes such as Exponential Moving Average (EMA) and Code Reset. In Stage 2, a GPT-based model constructs sequences of these code indices from pre-trained text embeddings, enhanced by a corruption strategy to minimize training-testing discrepancies.

Notably, the paper contrasts this methodology with recent diffusion-based approaches like MotionDiffuse, citing superior results in motion quality as evinced by a lower Fréchet Inception Distance (FID). Specifically, the authors report an FID of 0.116 on the HumanML3D dataset, significantly outperforming MotionDiffuse's reported FID of 0.630. These improvements highlight the efficacy of simple architectures when paired with robust training mechanisms and hyperparameter tuning.

Experimental Results and Discussions

A rigorous evaluation across HumanML3D and KIT-ML datasets substantiates the model’s capability. The proposed method shows competitive or superior performance across various metrics:

• R-Precision: The system achieves high text-motion consistency.
• FID: Indicating a strong synthesis of plausible human motion, the T2M-GPT model demonstrates notable improvements in this metric.

The paper offers an in-depth analysis concerning the size of the dataset and indicates that higher dataset volumes potentially lead to further enhancements in model performance, implying that the scalability of the model can handle increasing data with prospective benefits.

Implications for Future Research

From a theoretical perspective, the research reaffirms the viability of simpler architectures, like VQ-VAE, in tasks generally dominated by more complex diffusion-based models. Practically, the method opens pathways for application in industries reliant on motion capture, such as gaming and animation, offering a less costly and flexible alternative to traditional motion capture.

The paper furthers discussions on the role of innovative training techniques and hyperparameter settings in achieving state-of-the-art results, encouraging future explorations in this field. A significant implication is the model’s potential applicability in real-time systems, where efficient computation remains critical.

Conclusion

In concluding, this research positions T2M-GPT as a pivotal tool in bridging textual descriptions and human motion synthesis. While careful not to overstate the claims of novelty or revolutionary impact, the framework demonstrates a sound, practical solution to ongoing challenges in text-conditioned motion generation. Future work could expand on handling more complex and nuanced motion sequences or explore further enhancements in the corruption strategy to reduce any residual discrepancies between training scenarios and real-world applications. Thus, the paper contributes a valuable perspective and set of techniques to the vibrant field of multimodal AI and human motion synthesis.