We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

FlowFormer introduces a transformer architecture into optical flow estimation and achieves
state-of-the-art performance. The core component of FlowFormer is the transformer-based …

We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural
world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from …

Despite impressive performance for high-level downstream tasks, self-supervised pre-
training methods have not yet fully delivered on dense geometric vision tasks such as stereo …

Optical flow, or the estimation of motion fields from image sequences, is one of the
fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving …

Diffusion-based generative models are extremely effective in generating high-quality
images, with generated samples often surpassing the quality of those produced by other …

Self-supervised learning of visual representations has been focusing on learning content
features, which do not capture object motion or location, and focus on identifying and …

Recently, deep equilibrium models (DEQs) have drawn increasing attention from the
machine learning community. However, DEQs are much less understood in terms of certified …

Cascaded computation, whereby predictions are recurrently refined over several stages, has
been a persistent theme throughout the development of landmark detection models. In this …

Query-based object detectors directly decode image features into object instances with a set
of learnable queries. These query vectors are progressively refined to stable meaningful …