Search Results for author: Carsten Stoll

Found 8 papers, 1 papers with code

HUMOS: Human Motion Model Conditioned on Body Shape

1 code implementation5 Sep 2024 Shashank Tripathi, Omid Taheri, Christoph Lassner, Michael J. Black, Daniel Holden, Carsten Stoll

Generating realistic human motion is essential for many computer vision and graphics applications.

Diversity model

EPOCH: Jointly Estimating the 3D Pose of Cameras and Humans

no code implementations28 Jun 2024 Nicola Garau, Giulia Martinelli, Niccolò Bisagno, Denis Tomè, Carsten Stoll

These inputs are obtained from RegNet, which starts from a single image and provides estimates for the 2D pose and camera parameters.

3D Pose Estimation

fNeRF: High Quality Radiance Fields from Practical Cameras

no code implementations15 Jun 2024 Yi Hua, Christoph Lassner, Carsten Stoll, Iain Matthews

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 data.

3D Reconstruction

Personalized 3D Human Pose and Shape Refinement

no code implementations18 Mar 2024 Tom Wehrbein, Bodo Rosenhahn, Iain Matthews, Carsten Stoll

To address this issue, we propose to construct dense correspondences between initial human model estimates and the corresponding images that can be used to refine the initial predictions.

3D human pose and shape estimation

Speech Driven Tongue Animation

no code implementations CVPR 2022 Salvador Medina, Denis Tome, Carsten Stoll, Mark Tiede, Kevin Munhall, Alexander G. Hauptmann, Iain Matthews

In this work, we introduce a large-scale speech and mocap dataset that focuses on capturing tongue, jaw, and lip motion.

Decoder

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

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.

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