Beijing Academy of Artificial Intelligence (BAAI) introduces OmniGen2, a next-generation, open-source multimodal generative model. Expanding on its predecessor OmniGen, the new architecture unifies text-to-image generation, image editing, and subject-driven generation within a single transformer framework. It innovates by decoupling the modeling of text and image generation, incorporating a reflective training mechanism, and implementing a purpose-built…

Why Multimodal Reasoning Matters for Vision-Language Tasks Multimodal reasoning enables models to make informed decisions and answer questions by combining both visual and textual information. This type of reasoning plays a central role in interpreting charts, answering image-based questions, and understanding complex visual documents. The goal is to make machines capable of using vision as…

Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot,…

Despite the substantial progress in text-to-image (T2I) generation brought about by models such as DALL-E 3, Imagen 3, and Stable Diffusion 3, achieving consistent output quality — both in aesthetic and alignment terms — remains a persistent challenge. While large-scale pretraining provides general knowledge, it is insufficient to achieve high aesthetic quality and alignment. Supervised…

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing…

Video generation models have become a core technology for creating dynamic content by transforming text prompts into high-quality video sequences. Diffusion models, in particular, have established themselves as a leading approach for this task. These models work by starting from random noise and iteratively refining it into realistic video frames. Text-to-video (T2V) models extend this…

Recent advances in long-context (LC) modeling have unlocked new capabilities for LLMs and large vision-language models (LVLMs). Long-context vision–language models (LCVLMs) show an important step forward by enabling LVLMs to process hundreds of images and thousands of interleaved text tokens in a single forward pass. However, the development of effective evaluation benchmarks lags. It is…

Manipulating lighting conditions in images post-capture is challenging. Traditional approaches rely on 3D graphics methods that reconstruct scene geometry and properties from multiple captures before simulating new lighting using physical illumination models. Though these techniques provide explicit control over light sources, recovering accurate 3D models from single images remains a problem that frequently results in…

Artificial intelligence has grown beyond language-focused systems, evolving into models capable of processing multiple input types, such as text, images, audio, and video. This area, known as multimodal learning, aims to replicate the natural human ability to integrate and interpret varied sensory data. Unlike conventional AI models that handle a single modality, multimodal generalists are…

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly when operating directly on high-dimensional pixel data. Researchers have been investigating ways to optimize latent space representations to improve efficiency without compromising image quality. A critical problem in diffusion models is…