34 citations · 39 across the 11 of their papers we have counts for
12 papers · 1 filter
AMB3R-SLAM: Kilometer-scale SLAM with Hierarchical Backend
Hengyi Wang, Lourdes Agapito
We present AMB3R-SLAM, a real-time monocular SLAM system capable of reconstructing kilometer-scale trajectories over 10k frames on a single consumer-grade GPU. Our model couples a…
AMB3R: Accurate Feed-forward Metric-scale 3D Reconstruction with Backend
Hengyi Wang, Lourdes Agapito
We present AMB3R, a multi-view feed-forward model for dense 3D reconstruction on a metric-scale that addresses diverse 3D vision tasks. The key idea is to leverage a sparse, yet co…
DT-NVS: Diffusion Transformers for Novel View Synthesis
Wonbong Jang, Jonathan Tremblay, Lourdes Agapito
Generating novel views of a natural scene, e.g., every-day scenes both indoors and outdoors, from a single view is an under-explored problem, even though it is an organic extension…
Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
We address the 3D reconstruction of human faces from a single RGB image. To this end, we propose Pixel3DMM, a set of highly-generalized vision transformers which predict per-pixel…
Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors
Wonbong Jang, Philippe Weinzaepfel, Vincent Leroy +2
We present Pow3r, a novel large 3D vision regression model that is highly versatile in the input modalities it accepts. Unlike previous feed-forward models that lack any mechanism…
3D Reconstruction with Spatial Memory
Hengyi Wang, Lourdes Agapito
We present Spann3R, a novel approach for dense 3D reconstruction from ordered or unordered image collections. Built on the DUSt3R paradigm, Spann3R uses a transformer-based archite…
RDM: Recurrent Diffusion Model for Human Motion Generation
Mirgahney Mohamed, Harry Jake Cunningham, Marc P. Deisenroth +2
Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion methods have been proposed due to thei…
NPGA: Neural Parametric Gaussian Avatars
Simon Giebenhain, Tobias Kirschstein, Martin Rünz +2
The creation of high-fidelity, digital versions of human heads is an important stepping stone in the process of further integrating virtual components into our everyday lives. Cons…
NViST: In the Wild New View Synthesis from a Single Image with Transformers
Wonbong Jang, Lourdes Agapito
We propose NViST, a transformer-based model for efficient and generalizable novel-view synthesis from a single image for real-world scenes. In contrast to many methods that are tra…
MonoNPHM: Dynamic Head Reconstruction from Monocular Videos
Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos +3
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. To this end, we propose a latent appearance space that…
MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video
Hengyi Wang, Jingwen Wang, Lourdes Agapito
Neural rendering has demonstrated remarkable success in dynamic scene reconstruction. Thanks to the expressiveness of neural representations, prior works can accurately capture the…
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid
Mirgahney Mohamed, Lourdes Agapito
We propose DynamicSurf, a model-free neural implicit surface reconstruction method for high-fidelity 3D modelling of non-rigid surfaces from monocular RGB-D video. To cope with the…