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Found 24 papers, 10 papers with code
SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes
We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner.
Neural Lens Modeling
Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process.
Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments
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.
SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields
Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized by the scene's plenoptic function.
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.
TAVA: Template-free Animatable Volumetric Actors
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
In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis.
Virtual Elastic Objects
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
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.
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.
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.
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.
ANR: Articulated Neural Rendering for Virtual Avatars
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.
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
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.
TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video
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
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.
Ranked #3 on
3D Object Reconstruction From A Single Image
on BUFF
3D Object Reconstruction From A Single Image
3D Reconstruction
Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations
The goal of many computer vision systems is to transform image pixels into 3D representations.
Efficient Learning on Point Clouds with Basis Point Sets
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.
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Direct prediction of 3D body pose and shape remains a challenge even for highly parameterized deep learning models.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Towards Accurate Markerless Human Shape and Pose Estimation over Time
Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios.
A Generative Model of People in Clothing
We present the first image-based generative model of people in clothing for the full body.
Early Stopping without a Validation Set
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
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
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints.
Ranked #29 on
3D Human Pose Estimation
on HumanEva-I
