4 citations · 12 across the 10 of their papers we have counts for
11 papers
Scene-Centric Unsupervised Video Panoptic Segmentation
Christoph Reich, Oliver Hahn, Nikita Araslanov +4
Video panoptic segmentation (VPS) aims to jointly detect, segment, and track all objects while partitioning the video into semantically consistent regions. We introduce the task se…
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Aleksandar Jevtić, Christoph Reich, Felix Wimbauer +4
Semantic scene completion (SSC) aims to infer both the 3D geometry and semantics of a scene from single images. In contrast to prior work on SSC that heavily relies on expensive gr…
Scene-Centric Unsupervised Panoptic Segmentation
Oliver Hahn, Christoph Reich, Nikita Araslanov +3
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In con…
AnyCam: Learning to Recover Camera Poses and Intrinsics from Casual Videos
Felix Wimbauer, Weirong Chen, Dominik Muhle +2
Estimating camera motion and intrinsics from casual videos is a core challenge in computer vision. Traditional bundle-adjustment based methods, such as SfM and SLAM, struggle to pe…
Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks
Nina Shvetsova, Arsha Nagrani, Bernt Schiele +2
We propose a new "Unbiased through Textual Description (UTD)" video benchmark based on unbiased subsets of existing video classification and retrieval datasets to enable a more rob…
FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
Shangzhan Zhang, Jianyuan Wang, Yinghao Xu +5
We present FLARE, a feed-forward model designed to infer high-quality camera poses and 3D geometry from uncalibrated sparse-view images (i.e., as few as 2-8 inputs), which is a cha…
MaskInversion: Localized Embeddings via Optimization of Explainability Maps
Walid Bousselham, Sofian Chaybouti, Christian Rupprecht +2
Vision-language foundation models such as CLIP have achieved tremendous results in global vision-language alignment, but still show some limitations in creating representations for…
Cache Me if You Can: Accelerating Diffusion Models through Block Caching
Felix Wimbauer, Bichen Wu, Edgar Schoenfeld +11
Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However, one of the major drawbacks of diffusion…
Visual Geometry Grounded Deep Structure From Motion
Jianyuan Wang, Nikita Karaev, Christian Rupprecht +1
Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of uncons…
S4C: Self-Supervised Semantic Scene Completion with Neural Fields
Adrian Hayler, Felix Wimbauer, Dominik Muhle +2
3D semantic scene understanding is a fundamental challenge in computer vision. It enables mobile agents to autonomously plan and navigate arbitrary environments. SSC formalizes thi…
HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
Nina Shvetsova, Anna Kukleva, Xudong Hong +3
Instructional videos are a common source for learning text-video or even multimodal representations by leveraging subtitles extracted with automatic speech recognition systems (ASR…