The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

Abstract We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that
efficiently samples large-scale foundational image diffusion models in just 1–4 steps while …

We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from
sparse-view images in around 0.1 s. GRM is a feed-forward transformer-based model that …

Abstract 3D editing plays a crucial role in many areas such as gaming and virtual reality.
Traditional 3D editing methods which rely on representations like meshes and point clouds …

Diffusion models generate high-quality images but require dozens of forward passes. We
introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model …

Large-scale text-to-image generative models have been a ground-breaking development in
generative AI, with diffusion models showing their astounding ability to synthesize …

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs)
have become the preferred option for generative modeling, with numerous pre-trained …

Creating digital avatars from textual prompts has long been a desirable yet challenging task.
Despite the promising results achieved with 2D diffusion priors, current methods struggle to …

Denoising diffusion models have emerged as a powerful tool for various image generation
and editing tasks, facilitating the synthesis of visual content in an unconditional or input …

Recently, text-guided 3D generative methods have made remarkable advancements in
producing high-quality textures and geometry, capitalizing on the proliferation of large vision …