43 citations · 107 across the 19 of their papers we have counts for
22 papers
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…
Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments
Aljaž Božič, Denis Gladkov, Luke Doukakis +1
Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intrica…
SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields
Siddhant Ranade, Christoph Lassner, Kai Li +4
Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized by the scene's plenoptic function. This is achieved by using an MLP together with a mapping to a higher-…
NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation
Ziyan Wang, Giljoo Nam, Tuur Stuyck +6
The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. Both problems are highly challenging, because…
TAVA: Template-free Animatable Volumetric Actors
Ruilong Li, Julian Tanke, Minh Vo +4
Coordinate-based volumetric representations have the potential to generate photo-realistic virtual avatars from images. However, virtual avatars also need to be controllable even t…
Self-supervised Neural Articulated Shape and Appearance Models
Fangyin Wei, Rohan Chabra, Lingni Ma +6
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches ha…
Virtual Elastic Objects
Hsiao-yu Chen, Edgar Tretschk, Tuur Stuyck +4
We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions.…
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,…
Free-Viewpoint RGB-D Human Performance Capture and Rendering
Phong Nguyen-Ha, Nikolaos Sarafianos, Christoph Lassner +2
Capturing and faithfully rendering photo-realistic humans from novel views is a fundamental problem for AR/VR applications. While prior work has shown impressive performance captur…
ANR: Articulated Neural Rendering for Virtual Avatars
Amit Raj, Julian Tanke, James Hays +3
The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the r…
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik +3
We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a d…
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
Tiancheng Zhi, Christoph Lassner, Tony Tung +3
We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video. TexMesh enables high quality free-viewpoint rende…
ARCH: Animatable Reconstruction of Clothed Humans
Zeng Huang, Yuanlu Xu, Christoph Lassner +2
In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a m…
Pulsar: Efficient Sphere-based Neural Rendering
Christoph Lassner, Michael Zollhöfer
We propose Pulsar, an efficient sphere-based differentiable renderer that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integra…
Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations
Nadine Rueegg, Christoph Lassner, Michael J. Black +1
The goal of many computer vision systems is to transform image pixels into 3D representations. Recent popular models use neural networks to regress directly from pixels to 3D objec…
Efficient Learning on Point Clouds with Basis Point Sets
Sergey Prokudin, Christoph Lassner, Javier Romero
With the increased availability of 3D scanning technology, point clouds are moving into the focus of computer vision as a rich representation of everyday scenes. However, they are…
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Mohamed Omran, Christoph Lassner, Gerard Pons-Moll +2
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models. Mapping from the 2D image space to the prediction space is diffi…
A Generative Model of People in Clothing
Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler
We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-q…
Early Stopping without a Validation Set
Maren Mahsereci, Lukas Balles, Christoph Lassner +1
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good…
Unite the People: Closing the Loop Between 3D and 2D Human Representations
Christoph Lassner, Javier Romero, Martin Kiefel +3
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-the- wild". Ho…
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…