Enhancing Text-Driven Motion Generation with Language Understanding

In recent years, there has been a growing interest in text-driven human motion generation due to its potential applications in animation, film, VR/AR, and robotics. However, generating high-fidelity motions that align with text descriptions can be challenging as the language and motion data distributions differ inherently. To address this challenge, researchers have proposed a new paradigm that combines two modules: a motion tokenizer and a conditional masked motion transformer.
The motion tokenizer learns to transform 3D human motions into a sequence of discrete motion tokens without losing the rich semantic information. The conditional masked motion transformer is trained to predict randomly masked motion tokens conditioned on pre-computed text tokens. During inference, the transformer allows for parallel decoding of multiple motion tokens simultaneously while considering the context from both preceding and succeeding tokens.
The proposed paradigm aims to significantly improve synthesis quality, accelerate generation speed, and seamlessly preserve editability. However, there are some limitations to the model, such as struggling with fine-grain details for exceptionally long single textual descriptions and not supporting interactive motions involving multiple individuals.
To address these limitations, researchers plan to integrate large language models to segment lengthy text prompts into several concise prompts and explore how the model’s long motion generation capabilities can be leveraged. Additionally, they aim to support the generation of interactive motions involving multiple individuals in the future.
Overall, the proposed paradigm offers a promising approach to text-driven human motion generation, with potential applications in various fields. By demystifying complex concepts and using everyday language and engaging metaphors or analogies, this summary aims to provide an accessible understanding of the article’s key points without oversimplifying its essence.