Search Results for author:
Found 29 papers, 10 papers with code
A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles.
SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes
We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner.
HyperReel: High-Fidelity 6-DoF Video with Ray-Conditioned Sampling
Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques.
Ranked #1 on
Novel View Synthesis
on DONeRF: Evaluation Dataset
NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation
The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality.
HDHumans: A Hybrid Approach for High-fidelity Digital Humans
Photo-real digital human avatars are of enormous importance in graphics, as they enable immersive communication over the globe, improve gaming and entertainment experiences, and can be particularly beneficial for AR and VR settings.
Neural Pixel Composition: 3D-4D View Synthesis from Multi-Views
We present Neural Pixel Composition (NPC), a novel approach for continuous 3D-4D view synthesis given only a discrete set of multi-view observations as input.
KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
In this work, we investigate common issues with existing spatial encodings and propose a simple yet highly effective approach to modeling high-fidelity volumetric humans from sparse views.
Ranked #2 on
Generalizable Novel View Synthesis
on ZJU-MoCap
COAP: Compositional Articulated Occupancy of People
We present a novel neural implicit representation for articulated human bodies.
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception
We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction.
Mutual Scene Synthesis for Mixed Reality Telepresence
Remote telepresence via next-generation mixed reality platforms can provide higher levels of immersion for computer-mediated communications, allowing participants to engage in a wide spectrum of activities, previously not possible in 2D screen-based communication methods.
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture
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.
Learning Neural Light Fields with Ray-Space Embedding Networks
Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel.
A Deeper Look into DeepCap
Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.
Advances in Neural Rendering
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
NRST: Non-rigid Surface Tracking from Monocular Video
Our texture term exploits the orientation information in the micro-structures of the objects, e. g., the yarn patterns of fabrics.
Real-time Deep Dynamic Characters
We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery.
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face.
Ranked #2 on
3D Face Animation
on VOCASET
Neural 3D Video Synthesis from Multi-view Video
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.
Mixture of Volumetric Primitives for Efficient Neural Rendering
Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications.
A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose
We propose a method to learn a generative neural body model from unlabelled monocular videos by extending Neural Radiance Fields (NeRFs).
PVA: Pixel-aligned Volumetric Avatars
Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.
DeepCap: Monocular Human Performance Capture Using Weak Supervision
Human performance capture is a highly important computer vision problem with many applications in movie production and virtual/augmented reality.
Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation
In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space.
3D Morphable Face Models -- Past, Present and Future
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.
Real-Time Global Illumination Decomposition of Videos
We propose the first approach for the decomposition of a monocular color video into direct and indirect illumination components in real time.
LiveCap: Real-time Human Performance Capture from Monocular Video
Our method is the first real-time monocular approach for full-body performance capture.
Neural Rendering and Reenactment of Human Actor Videos
In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person.
Mo2Cap2: Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye Camera
We tackle these challenges based on a novel lightweight setup that converts a standard baseball cap to a device for high-quality pose estimation based on a single cap-mounted fisheye camera.
Ranked #6 on
Egocentric Pose Estimation
on GlobalEgoMocap Test Dataset
(using extra training data)
LIME: Live Intrinsic Material Estimation
We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input.
