296 citations · 306 across the 10 of their papers we have counts for
11 papers
Long-horizon Video Generation with Slot Memory Addressed By Region Tokens
Eren Cetin, Julius Erbach, Vukasin Bozic +6
Memory for long-horizon video generation must satisfy two competing requirements: it must remain bounded so generation can continue indefinitely, while still recovering what was ob…
PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
Haofei Xu, Rundi Wu, Philipp Henzler +7
State-of-the-art single-image 3D reconstruction methods often rely on complex hybrid architectures and loss functions, or compress geometry into latent spaces in order to leverage…
Unified Panoramic Geometry Estimation via Multi-View Foundation Models
Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer +4
Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct 3D scene structure not only fr…
Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas
Felix Wimbauer, Fabian Manhardt, Michael Oechsle +4
The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and wor…
Understanding, Accelerating, and Improving MeanFlow Training
Jin-Young Kim, Hyojun Go, Lea Bogensperger +5
MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear…
CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation
Nikolai Kalischek, Michael Oechsle, Fabian Manhardt +3
We introduce a novel method for generating 360° panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion mod…
TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape Generation
Nikolai Kalischek, Torben Peters, Jan D. Wegner +1
Probabilistic denoising diffusion models (DDMs) have set a new standard for 2D image generation. Extending DDMs for 3D content creation is an active field of research. Here, we pro…
BiasBed -- Rigorous Texture Bias Evaluation
Nikolai Kalischek, Rodrigo C. Daudt, Torben Peters +3
The well-documented presence of texture bias in modern convolutional neural networks has led to a plethora of algorithms that promote an emphasis on shape cues, often to support ge…
Satellite-based high-resolution maps of cocoa planted area for Côte d'Ivoire and Ghana
Nikolai Kalischek, Nico Lang, Cécile Renier +7
Côte d'Ivoire and Ghana, the world's largest producers of cocoa, account for two thirds of the global cocoa production. In both countries, cocoa is the primary perennial crop, prov…
In the light of feature distributions: moment matching for Neural Style Transfer
Nikolai Kalischek, Jan Dirk Wegner, Konrad Schindler
Style transfer aims to render the content of a given image in the graphical/artistic style of another image. The fundamental concept underlying NeuralStyle Transfer (NST) is to int…
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
Nico Lang, Nikolai Kalischek, John Armston +3
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While G…