We present Muse, a text-to-image Transformer model that achieves state-of-the-art image
generation performance while being significantly more efficient than diffusion or …

Current machine learning models for vision are often highly specialized and limited to a
single modality and task. In contrast, recent large language models exhibit a wide range of …

We introduce SynCLR a novel approach for learning visual representations exclusively from
synthetic images without any real data. We synthesize a large dataset of image captions …

Abstract We introduce Generative Infinite-Vocabulary Transformers (GIVT) which generate
vector sequences with real-valued entries, instead of discrete tokens from a finite …

General world models represent a crucial pathway toward achieving Artificial General
Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual …

The field of image synthesis is currently flourishing due to the advancements in diffusion
models. While diffusion models have been successful their computational intensity has …

Recent studies have shown that the denoising process in (generative) diffusion models can
induce meaningful (discriminative) representations inside the model, though the quality of …

We introduce MoMask a novel masked modeling framework for text-driven 3D human
motion generation. In MoMask a hierarchical quantization scheme is employed to represent …

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

Conditional 3D generation is undergoing a significant advancement, enabling the free
creation of 3D content from inputs such as text or 2D images. However, previous …