1 citations · 2 across the 5 of their papers we have counts for
5 papers
fNeRF: High Quality Radiance Fields from Practical Cameras
Yi Hua, Christoph Lassner, Carsten Stoll +1
In recent years, the development of Neural Radiance Fields has enabled a previously unseen level of photo-realistic 3D reconstruction of scenes and objects from multi-view camera d…
SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes
Edith Tretschk, Vladislav Golyanik, Michael Zollhoefer +3
Existing methods for the 4D reconstruction of general, non-rigidly deforming objects focus on novel-view synthesis and neglect correspondences. However, time consistency enables ad…
Neural Lens Modeling
Wenqi Xian, Aljaž Božič, Noah Snavely +1
Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limi…
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture
Ziyan Wang, Giljoo Nam, Tuur Stuyck +4
Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance.Yet,…
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Federica Bogo, Angjoo Kanazawa, Christoph Lassner +3
We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show…