22 citations · 55 across the 10 of their papers we have counts for
14 papers
DuoMo: Dual Motion Diffusion for World-Space Human Reconstruction
Yufu Wang, Evonne Ng, Soyong Shin +8
We present DuoMo, a generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Reconstructing such mot…
Manifold Sampling for Differentiable Uncertainty in Radiance Fields
Linjie Lyu, Ayush Tewari, Marc Habermann +4
Radiance fields are powerful and, hence, popular models for representing the appearance of complex scenes. Yet, constructing them based on image observations gives rise to ambiguit…
3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt
Lukas Höllein, Aljaž Božič, Michael Zollhöfer +1
We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored Levenberg-Marquardt (LM). Exist…
ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models
Lukas Höllein, Aljaž Božič, Norman Müller +5
3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-i…
SpecNeRF: Gaussian Directional Encoding for Specular Reflections
Li Ma, Vasu Agrawal, Haithem Turki +5
Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance…
HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
Haithem Turki, Vasu Agrawal, Samuel Rota Bulò +5
Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering, thus requiring many samp…
Drivable 3D Gaussian Avatars
Wojciech Zielonka, Timur Bagautdinov, Shunsuke Saito +3
We present Drivable 3D Gaussian Avatars (D3GA), a multi-layered 3D controllable model for human bodies that utilizes 3D Gaussian primitives embedded into tetrahedral cages. The adv…
Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering
Linjie Lyu, Ayush Tewari, Marc Habermann +4
Inverse rendering, the process of inferring scene properties from images, is a challenging inverse problem. The task is ill-posed, as many different scene configurations can give r…
AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
Andreas Kurz, Thomas Neff, Zhaoyang Lv +2
Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow…
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction
Guy Gafni, Justus Thies, Michael Zollhöfer +1
We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. Digitally modeling and reconstructing a talking human is a key building-block fo…
Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction
Aljaž Božič, Pablo Palafox, Michael Zollhöfer +3
We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. Specifically, we implicitly model a deformation grap…
Neural Non-Rigid Tracking
Aljaž Božič, Pablo Palafox, Michael Zollhöfer +3
We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables state-of-the-art non-rigid reconstruction by a learned robust optimization. Given two inpu…
State of the Art on Neural Rendering
Ayush Tewari, Ohad Fried, Justus Thies +16
Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic imag…
Commodity RGB-D Sensors: Data Acquisition
Michael Zollhöfer
Over the past ten years we have seen a democratization of range sensing technology. While previously range sensors have been highly expensive and only accessible to a few domain ex…