6 papers
StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space
Tjark Behrens, Anton Obukhov, Bingxin Ke +3
We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without explicit depth or warpin…
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking
Zixiang Zhao, Haowen Bai, Bingxin Ke +5
The real world is dynamic, yet most image fusion methods process static frames independently, ignoring temporal correlations in videos and leading to flickering and temporal incons…
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Massimiliano Viola, Kevin Qu, Nando Metzger +4
Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constra…
Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
Bingxin Ke, Kevin Qu, Tianfu Wang +5
The success of deep learning in computer vision over the past decade has hinged on large labeled datasets and strong pretrained models. In data-scarce settings, the quality of thes…
Video Depth without Video Models
Bingxin Ke, Dominik Narnhofer, Shengyu Huang +5
Video depth estimation lifts monocular video clips to 3D by inferring dense depth at every frame. Recent advances in single-image depth estimation, brought about by the rise of lar…
BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation
Xiang Zhang, Bingxin Ke, Hayko Riemenschneider +5
By training over large-scale datasets, zero-shot monocular depth estimation (MDE) methods show robust performance in the wild but often suffer from insufficient detail. Although re…