DiPIR is an innovative technology designed to address the technical challenges of seamlessly inserting virtual objects into real scenes. It achieves this by combining large-scale diffusion models with physics-based inverse rendering processes to accurately recover scene lighting information from a single image, allowing for the natural integration of virtual objects with their environments. The workflow of DiPIR includes constructing virtual 3D scenes, simulating interactions between objects and environments, and optimizing lighting conditions through diffusion models to ensure that generated images conform to real scenes. Its advantages include wide applicability, suitable for scenes under different lighting conditions, and the generation of highly realistic images.

Multimodal generative models are leading a new trend in artificial intelligence, focusing on the integration of visual and textual data to create multifunctional AI systems that perform various tasks, from image generation to understanding and reasoning across different data types. A key challenge is to enhance the capabilities of autoregressive (AR) models so they can generate high-detail images based on textual descriptions. While diffusion models excel in generating high-quality images, AR models lag behind in image quality, resolution flexibility, and multitasking capabilities. Researchers from Shanghai AI Lab and The Chinese University of Hong Kong have introduced Lum

A collaborative effort between the University of Alberta, the University of Toronto, and Huawei's Noah's Ark Lab has led to the development of a new method called TexGen, aimed at solving the challenge of generating realistic textures in the field of 3D modeling. This technology generates corresponding 3D textures from text descriptions, significantly outperforming existing techniques. The core involves a multi-view sampling and re-sampling framework combined with a pre-trained text-to-image diffusion model. This method effectively reduces discrepancies between views by maintaining a texture map with RGB parameters and updating it after each sampling, achieving view-consistent texture generation. An attention guidance mechanism has been introduced.

Stable-Hair is an innovative hair transfer technology designed to address challenges in handling diverse and complex hairstyles, providing users with an enhanced virtual hairstyle try-on experience. Utilizing diffusion models, it accurately transfers various real hairstyles onto user-provided facial images. The workflow consists of two phases: first, a 'baldness converter' removes hair from the image, followed by hairstyle transfer through three ....

In the realm of contemporary music creation, the application of AI technology is garnering increasing attention. However, the complexities of copyright issues and fair compensation for artists pose significant challenges for many startups in this field. Yet, Indian startup Beatoven.ai appears to have found a morally and responsibly sound approach to address these challenges.Mansoor Rahimat Khan, the co-founder and CEO of Beatoven.ai, is a professional sitar player hailing from a family with a se

Researchers from the University of Hong Kong and Tencent have proposed a new paradigm for multimodal recommendation systems called DiffMM, aiming to enhance the accuracy of short video recommendations. The system creates a graph containing user and video information and utilizes graph diffusion and contrastive learning techniques to better understand the relationship between users and videos, thereby achieving more accurate recommendations.The model method of DiffMM primarily includes three part

In recent research, a new method called Flash Diffusion has brought revolutionary breakthroughs to image generation technology. This method accelerates the generation process of pre-trained diffusion models by training predictive models to generate denoised multi-step prediction results in a single step.