Recently diffusion-based methods like InstructPix2Pix (IP2P) have achieved effective
instruction-based image editing requiring only natural language instructions from the user …

This work addresses the challenge of high-quality surface normal estimation from monocular
colored inputs (ie, images and videos), a field which has recently been revolutionized by …

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes
too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable …

We introduce DragAPart, a method that, given an image and a set of drags as input,
generates a new image of the same object that responds to the action of the drags …

This paper introduces a novel unified representation of diffusion models for image
generation and segmentation. Specifically we use a colormap to represent entity-level …

While pre-trained large-scale vision models have shown significant promise for semantic
correspondence their features often struggle to grasp the geometry and orientation of …

Personalized retrieval and segmentation aim to locate specific instances within a dataset
based on an input image and a short description of the reference instance. While supervised …

Owing to rapid developments in driverless technologies, vision tasks for unmanned vehicles
have gained considerable attention, particularly in multimodal-based urban scene parsing …

Significant strides have been made using large vision-language models, like Stable
Diffusion (SD), for a variety of downstream tasks, including image editing, image …

Diffusion models represent a new paradigm in text-to-image generation. Beyond generating
high-quality images from text prompts models such as Stable Diffusion have been …