We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies
pre-trained text-image diffusion and discriminative models to perform open-vocabulary …

We propose a method for editing images from human instructions: given an input image and
a written instruction that tells the model what to do, our model follows these instructions to …

Deep generative models are becoming increasingly powerful, now generating diverse high
fidelity photo-realistic samples given text prompts. Have they reached the point where …

Synthesizing visual content that meets users' needs often requires flexible and precise
controllability of the pose, shape, expression, and layout of the generated objects. Existing …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Preparing training data for deep vision models is a labor-intensive task. To address this,
generative models have emerged as an effective solution for generating synthetic data …

Many fine-grained classification tasks, like rare animal identification, have limited training
data and consequently classifiers trained on these datasets often fail to generalize to …

Semantic segmentation assigns category labels to each pixel in an image, enabling
breakthroughs in fields such as autonomous driving and robotics. Deep Neural Networks …

Annotating images with pixel-wise labels is a time-consuming and costly process. Recently,
DatasetGAN showcased a promising alternative-to synthesize a large labeled dataset via a …

We present a one-shot method to infer and render a photorealistic 3D representation from a
single unposed image (eg, face portrait) in real-time. Given a single RGB input, our image …