Motion GPT: text to motion

Introduction: Exploring MotionGPT for Realistic Animation

MotionGPT is a groundbreaking model developed by Microsoft Research Asia and Peking University, enabling the generation of lifelike animations from a single input image. Discover how it empowers users to produce high-quality animations depicting human and animal movements, such as walking, running, dancing, and jumping.

Unveiling the Magic of MotionGPT: Harnessing Pre-Trained GPT-3

MotionGPT leverages the concept of a pre-trained GPT-3 model, utilizing it to grasp motion patterns by learning from an extensive video dataset. Through encoding the input image into a latent representation, MotionGPT’s decoder generates a sequence of latent codes representing different frames of the animation. These codes are then decoded into images via a convolutional neural network.

Method overview: MotionGPT consists of a motion tokenizer V (Sec. 3.1) and a motionaware language model (Sec. 3.2). Combining Motion Tokens learned by V and Text Tokens by text tokenizer, we then learn motion and language jointly utilizing language model as backbone.

Preserving Identity and Consistency: Conquering Animation Challenges

Maintaining the identity and appearance of the input image throughout the animation is a significant hurdle. MotionGPT tackles this challenge with an innovative attention mechanism. This mechanism enables the model to attend to the input image and previous frames during generation, ensuring coherence and consistency throughout the animation.

Embracing Diversity and Natural Motion: Conquering Animation Challenges

Generating diverse and natural motions that align with the input image is another crucial aspect. MotionGPT overcomes this hurdle by adopting a conditional variational autoencoder (CVAE) framework. This framework captures the variability and uncertainty of human and animal motions, allowing the model to sample different latent codes from the CVAE prior distribution and generate various animations for the same input image.

Achieving Excellence: MotionGPT’s Outstanding Performance

MotionGPT sets new benchmarks for animation quality, surpassing existing methods on esteemed datasets like Human3.6M, Penn Action, and Animal Pose. The model generates smooth, realistic, and diverse animations that effortlessly handle different poses, viewpoints, and backgrounds. It’s superior user preference and perceptual quality further solidify its exceptional performance.

Exploring Further

For an in-depth understanding of MotionGPT, we recommend reading the complete paper available at To witness the incredible animations created by MotionGPT, check out the examples showcased in this video: MotionGPT revolutionizes content creation by introducing an exciting new approach to effortlessly generate engaging and creative animations.