#MotionGPT

The Bert-VITS2-ext project expands the capabilities of the original Bert-VITS2 by generating synchronized facial expressions with TTS outputs, aimed at improving emotion recognition. This project focuses on incorporating facial data into TTS training to enhance expression accuracy across frameworks such as CosyVoice and GPT-SoVITS. It allows for the collection of synchronized audio and facial data, enabling the training of models to convert audio into expressions and providing audio-to-photoreal interfaces. Additionally, the project delves into generating body animations using MotionGPT, which encounters some language translation challenges. This methodology facilitates more realistic digital interactions.

Discover the innovative use of optimized language models to generate versatile motions. MotionGPT seamlessly converts text into motion using pretrained models and datasets like HumanML3D. Learn about model installation, training, evaluation, and utilize visualization tools for motion and SMPL mesh rendering. Explore how MotionGPT leverages VQ-VAE and LLaMA models to match human motion descriptions, providing precise AI-driven solutions for researchers and developers in animation.

MotionGPT offers a novel approach to integrating human motion with language, supporting diverse tasks like motion generation and prediction through its motion-language model. Employing discrete vector quantization, it regards human motion as a language, facilitating the convergence of these modalities. Pre-trained on extensive data, MotionGPT is optimized for tasks including motion captioning and in-between state generation, showcasing its proficiency with language model and instruction tuning advancements.