1 citations · 2 across the 13 of their papers we have counts for
15 papers
Image Tokenizers Should Be Trained Between Images
Ole Beißwenger, Jan-Niklas Dihlmann, Hendrik Lensch
Latent diffusion models generate images as codes of an image tokenizer, whose decoder turns them into the pixel image. Recent tokenizers improve how well these codes reconstruct an…
InvSplat: Inverse Feed-Forward Scene Splatting
Polina Karpikova, Wenjing Bian, Haofei Xu +2
Inverse rendering aims to recover both 3D geometry and physically meaningful material properties from images, enabling applications such as relighting and novel view synthesis. Opt…
AEGIR: Modeling Area Emitters for Indoor Inverse Rendering using Gaussian Splatting
Mohamed Shawky Sabae, Philipp Langsteiner, Jan-Niklas Dihlmann +1
Inverse rendering requires separating illumination from surface materials, which is highly ambiguous due to their tight coupling in observed images. While Gaussian Splatting is eff…
From Phase to Phenomenon: Self-Supervised Learning of Subsurface Scattering with Minimal Phase-shift Inputs
Arjun Majumdar, Raphael Braun, Andreas Engelhardt +1
We propose a self-supervised pretraining framework for learning sub-surface scattering (SSS) light transport representations from minimal input. Our method leverages a stereo proje…
Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation
Jan-Niklas Dihlmann, Andreas Engelhardt, Simon Donne +2
Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy or avoid. In authoring, this…
Neural Acquisition & Representation of Subsurface Scattering
Arjun Majumdar, Raphael Braun, Hendrik Lensch
We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each poi…
ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination
Jan-Niklas Dihlmann, Mark Boss, Simon Donne +3
Reconstructing 3D assets from images has long required separate pipelines for geometry reconstruction, material estimation, and illumination recovery, each with distinct limitation…
MatSpray: Fusing 2D Material World Knowledge on 3D Geometry
Philipp Langsteiner, Jan-Niklas Dihlmann, Hendrik P. A. Lensch
Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have ena…
3D-RE-GEN: 3D Reconstruction of Indoor Scenes with a Generative Framework
Tobias Sautter, Jan-Niklas Dihlmann, Hendrik P. A. Lensch
Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for…
SViM3D: Stable Video Material Diffusion for Single Image 3D Generation
Andreas Engelhardt, Mark Boss, Vikram Voleti +3
We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffus…
CharGen: Fast and Fluent Portrait Modification
Jan-Niklas Dihlmann, Arnela Killguss, Hendrik P. A. Lensch
Interactive editing of character images with diffusion models remains challenging due to the inherent trade-off between fine-grained control, generation speed, and visual fidelity.…
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
Deheng Zhang, Jingyu Wang, Shaofei Wang +4
In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enablin…
Subsurface Scattering for 3D Gaussian Splatting
Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt +2
3D reconstruction and relighting of objects made from scattering materials present a significant challenge due to the complex light transport beneath the surface. 3D Gaussian Splat…
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild
Andreas Engelhardt, Amit Raj, Mark Boss +8
We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inv…
SIGNeRF: Scene Integrated Generation for Neural Radiance Fields
Jan-Niklas Dihlmann, Andreas Engelhardt, Hendrik Lensch
Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs), they enab…