Real ones will know that Mount Rainier looks too big in this image, but the re-creation of a Washington State ferry in this AI image is uncanny. This is The Ste...

AI Surveillance on British Roads On a grey morning along the A38 near Plymouth, a white van equipped with twin cameras captures thousands of images per hour, i...

Introduction An AI background remover can feel almost magical when it works well—and frustrating when it doesn’t. The difference usually comes down to two thin...

The recent success of 3D Gaussian Splatting (3DGS) has reshaped novel view synthesis by enabling fast optimization and real-time rendering of high-quality radia...

Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. Howeve...

We present Particulate, a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying arti...

The collection of large-scale and diverse robot demonstrations remains a major bottleneck for imitation learning, as real-world data acquisition is costly and s...

Reality is a dance between rigid constraints and deformable structures. For video models, that means generating motion that preserves fidelity as well as struct...

Accurately quantifying vitiligo extent in routine clinical photographs is crucial for longitudinal monitoring of treatment response. We propose a trustworthy, f...

Video matting remains limited by the scale and realism of existing datasets. While leveraging segmentation data can enhance semantic stability, the lack of effe...

Model fingerprint detection techniques have emerged as a promising approach for attributing AI-generated images to their source models, but their robustness und...

Generating realistic synthetic microscopy images is critical for training deep learning models in label-scarce environments, such as cell counting with many cel...

Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perc...

Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggl...

Key frame selection in video understanding presents significant challenges. Traditional top-K selection methods, which score frames independently, often fail to...

The growing demand for real-time DNN applications on edge devices necessitates faster inference of increasingly complex models. Although many devices include sp...

We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without expl...

Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physicall...

The success of foundation models in language and vision motivated research in fully end-to-end robot navigation foundation models (NFMs). NFMs directly map mono...

Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen contexts. However...

We propose a decoupled 3D scene generation framework called SceneMaker in this work. Due to the lack of sufficient open-set de-occlusion and pose estimation pri...

Normalizing Flows (NFs) have been established as a principled framework for generative modeling. Standard NFs consist of a forward process and a reverse process...

In this work, we explore an untapped signal in diffusion model inference. While all previous methods generate images independently at inference, we instead ask ...

Self-supervised pre-training has revolutionized foundation models for languages, individual 2D images and videos, but remains largely unexplored for learning 3D...