AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head
AudioGPT is a multi-modal AI system that complements LLMs with foundation models to process complex audio information and solve numerous understanding and generation tasks, along with input/output interface to support spoken dialogue. It empowers humans to create rich and diverse audio content with unprecedented ease.
Businesses can use AudioGPT to automate customer service and support activities with human-like conversations, generate personalized audio content, and improve accessibility for visually-impaired users through speech-to-text and text-to-speech functionalities.
Patch-based 3D Natural Scene Generation from a Single Example
This paper proposes a patch-based 3D generative model that can synthesize high-quality general natural scenes with both realistic geometric structure and visual appearance from a single example, addressing unique challenges arising from lifting classical 2D patch-based framework to 3D generation.
Businesses can use the patch-based 3D scene generation model to create realistic 3D visualizations of their products and services in various natural scenes, such as homes, offices, or outdoors, without the need for extensive training data, reducing the production cost and time.
Towards Realistic Generative 3D Face Models
This paper proposes a 3D controllable generative face model that can produce high-quality albedo and precise 3D shape leveraging existing 2D generative models. It outperforms the state-of-the-art methods in the well-known NoW benchmark for shape reconstruction and enables editing of detailed 3D rendered faces, including direct control of expressions in 3D faces by exploiting latent space leading to text-based editing of 3D faces.
Businesses can use the 3D generative face model to create realistic digital avatars for gaming, animation, or fashion industries, generate synthetic data for face recognition and biometric authentication systems, and improve accessibility for facially-impaired users through facial recognition and reconstruction functionalities.
Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
Patch Diffusion is a patch-wise training framework that reduces the training time costs while improving data efficiency, democratizing diffusion model training to broader users. Through Patch Diffusion, we could achieve faster training while maintaining comparable or better generation quality.
Businesses can leverage Patch Diffusion to enable faster and more data-efficient training of diffusion models, leading to better performance and quality in image and video processing applications.
Answering Questions by Meta-Reasoning over Multiple Chains of Thought
Multi-Chain Reasoning (MCR) is an approach that prompts large language models to meta-reason over multiple chains of thought in multi-hop question-answering (QA) tasks, outperforming strong baselines on 7 different datasets.
Businesses that rely on QA systems can leverage MCR to enhance their systems' performance and accuracy, leading to better decision-making and customer support.