We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …

While next-token prediction is considered a promising path towards artificial general
intelligence, it has struggled to excel in multimodal tasks, which are still dominated by …

We introduce Free3D a simple accurate method for monocular open-set novel view
synthesis (NVS). Similar to Zero-1-to-3 we start from a pre-trained 2D image generator for …

In this paper, we introduce Janus, an autoregressive framework that unifies multimodal
understanding and generation. Prior research often relies on a single visual encoder for …

Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is
increasingly used in representation learning. However, optimizing the codevectors in …

Existing vector quantization (VQ) based autoregressive models follow a two-stage
generation paradigm that first learns a codebook to encode images as discrete codes, and …

Text-to-image generation is a significant domain in modern computer vision and has
achieved substantial improvements through the evolution of generative architectures …

Masked transformer models for class-conditional image generation have become a
compelling alternative to diffusion models. Typically comprising two stages-an initial VQGAN …

We present Visual AutoRegressive modeling (VAR), a new generation paradigm that
redefines the autoregressive learning on images as coarse-to-fine" next-scale prediction" or" …

Existing autoregressive models follow the two-stage generation paradigm that first learns a
codebook in the latent space for image reconstruction and then completes the image …