Search Results for author: Christoph Lassner

Found 24 papers, 10 papers with code

Neural Lens Modeling

no code implementations CVPR 2023 Wenqi Xian, Aljaž Božič, Noah Snavely, Christoph Lassner

Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process.

3D Reconstruction Camera Calibration

Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments

no code implementations12 Dec 2022 Aljaž Božič, Denis Gladkov, Luke Doukakis, Christoph Lassner

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.

Neural Rendering Object

SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields

no code implementations7 Dec 2022 Siddhant Ranade, Christoph Lassner, Kai Li, Christian Haene, Shen-Chi Chen, Jean-Charles Bazin, Sofien Bouaziz

Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized by the scene's plenoptic function.

Video Editing

TAVA: Template-free Animatable Volumetric Actors

1 code implementation17 Jun 2022 RuiLong Li, Julian Tanke, Minh Vo, Michael Zollhofer, Jurgen Gall, Angjoo Kanazawa, Christoph Lassner

Since TAVA does not require a body template, it is applicable to humans as well as other creatures such as animals.

Self-supervised Neural Articulated Shape and Appearance Models

no code implementations CVPR 2022 Fangyin Wei, Rohan Chabra, Lingni Ma, Christoph Lassner, Michael Zollhöfer, Szymon Rusinkiewicz, Chris Sweeney, Richard Newcombe, Mira Slavcheva

In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis.

Novel View Synthesis

Virtual Elastic Objects

no code implementations CVPR 2022 Hsiao-yu Chen, Edgar Tretschk, Tuur Stuyck, Petr Kadlecek, Ladislav Kavan, Etienne Vouga, Christoph Lassner

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.

Free-Viewpoint RGB-D Human Performance Capture and Rendering

no code implementations27 Dec 2021 Phong Nguyen-Ha, Nikolaos Sarafianos, Christoph Lassner, Janne Heikkila, Tony Tung

While prior work has shown impressive performance capture results in laboratory settings, it is non-trivial to achieve casual free-viewpoint human capture and rendering for unseen identities with high fidelity, especially for facial expressions, hands, and clothes.

Neural Rendering Novel View Synthesis

HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

no code implementations CVPR 2022 Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhoefer, Jessica Hodgins, Christoph Lassner

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, hair is a critical component for believable avatars.

Neural Rendering Optical Flow Estimation

Neural 3D Video Synthesis from Multi-view Video

1 code implementation CVPR 2022 Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, Zhaoyang Lv

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation.

Motion Interpolation

ANR: Articulated Neural Rendering for Virtual Avatars

no code implementations CVPR 2021 Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, Christoph Lassner

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) provides a compelling balance between computational complexity and realism of the resulting images.

Neural Rendering

Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video

2 code implementations ICCV 2021 Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Christoph Lassner, Christian Theobalt

We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e. g. a `bullet-time' video effect.

Novel View Synthesis Video Editing

TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video

no code implementations ECCV 2020 Tiancheng Zhi, Christoph Lassner, Tony Tung, Carsten Stoll, Srinivasa G. Narasimhan, Minh Vo

We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video.

ARCH: Animatable Reconstruction of Clothed Humans

1 code implementation CVPR 2020 Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, Tony Tung

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 monocular image.

3D Object Reconstruction From A Single Image 3D Reconstruction

Efficient Learning on Point Clouds with Basis Point Sets

1 code implementation ICCV 2019 Sergey Prokudin, Christoph Lassner, Javier Romero

The basis point set representation is a residual representation that can be computed efficiently and can be used with standard neural network architectures and other machine learning algorithms.

BIG-bench Machine Learning

Towards Accurate Markerless Human Shape and Pose Estimation over Time

no code implementations24 Jul 2017 Yinghao Huang, Federica Bogo, Christoph Lassner, Angjoo Kanazawa, Peter V. Gehler, Ijaz Akhter, Michael J. Black

Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios.

Pose Estimation

A Generative Model of People in Clothing

1 code implementation ICCV 2017 Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler

We present the first image-based generative model of people in clothing for the full body.

Semantic Segmentation

Early Stopping without a Validation Set

no code implementations28 Mar 2017 Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig

Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization.


Unite the People: Closing the Loop Between 3D and 2D Human Representations

2 code implementations CVPR 2017 Christoph Lassner, Javier Romero, Martin Kiefel, Federica Bogo, Michael J. Black, Peter V. Gehler

With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.

 Ranked #1 on Monocular 3D Human Pose Estimation on Human3.6M (Use Video Sequence metric)

3D human pose and shape estimation Monocular 3D Human Pose Estimation

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