Reinforcement learning has seen significant success in recent years, but its low sample efficiency limits its application in the real world. World models, as a type of environment generation model, offer hope for addressing this issue. They can serve as simulated environments to train reinforcement learning agents with higher sample efficiency. Currently, most world models simulate environment dynamics through discrete latent variable sequences. However, this method of compressing into a compact discrete representation may overlook visual details that are critical for reinforcement learning. Meanwhile, diffusion models have become prominent in the field of image generation.

Recent research from Alibaba's Tongyi Laboratory indicates that existing text-to-image Diffusion Transformer models already possess the ability to generate multiple images with specific relational contexts. With just a little 'guidance', they can achieve a synthesis of understanding and produce high-quality multi-image collections. Traditional Diffusion models are more like 'rote learning' students, requiring massive datasets to generate high-quality images. However, with the support of IC-LoRA, they become 'high-achieving students', capable of learning through analogy.

A recent study proposed a new method that leverages human attention to enhance the quality of AI-generated images. This method uses a saliency detector to identify the most important areas in the image and prioritizes the generation of these areas. Traditional image generation methods optimize the entire image uniformly, whereas the new method identifies and prioritizes more 'important' regions, similar to human attention. This approach can improve image quality and fidelity to textual prompts. Researchers utilized stable diffusion models and saliency detectors to generate images, and

At the 2024 Volcano Engine AI Innovation Roadshow, Volcano Engine President Tan Dai unveiled the latest Doubao · Video Generation Model. This model incorporates several advanced technologies, including the efficient DIT Fusion Computing Unit, which enables deep compression encoding of video and text; it also employs a novel diffusion model training approach to ensure consistency in generating multi-camera videos; in addition, the model integrates a deeply optimized Transformer architecture, significantly enhancing the generalization ability of video generation.

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.