Search Results for author:
Found 23 papers, 6 papers with code
SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics.
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy.
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction
This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body.
NSF: Neural Surface Fields for Human Modeling from Monocular Depth
Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining.
VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs
Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially consistent manner.
Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)
Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays.
Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields
The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres.
BodyMap: Learning Full-Body Dense Correspondence Map
Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction.
Neural Rendering of Humans in Novel View and Pose from Monocular Video
We accomplish this by utilizing both the human pose that models the body shape as well as point clouds that partially cover the human as input.
SPAMs: Structured Implicit Parametric Models
We observe that deformable object motion is often semantically structured, and thus propose to learn Structured-implicit PArametric Models (SPAMs) as a deformable object representation that structurally decomposes non-rigid object motion into part-based disentangled representations of shape and pose, with each being represented by deep implicit functions.
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.
Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing
Neural-GIF can be trained on raw 3D scans and reconstructs detailed complex surface geometry and deformations.
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited
We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles.
Ranked #1 on
3D Object Reconstruction From A Single Image
on RenderPeople
(using extra training data)
3D Object Reconstruction From A Single Image
Image-to-Image Translation
Data-Driven 3D Reconstruction of Dressed Humans From Sparse Views
We propose a data-driven end-to-end approach that reconstructs an implicit 3D representation of dressed humans from sparse camera views.
Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans
Given a segmentation mask defining the layout of the semantic regions in the texture map, our network generates high-resolution textures with a variety of styles, that are then used for rendering purposes.
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.
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
SizerNet allows to estimate and visualize the dressing effect of a garment in various sizes, and ParserNet allows to edit clothing of an input mesh directly, removing the need for scan segmentation, which is a challenging problem in itself.
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
DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare
We present DenseRaC, a novel end-to-end framework for jointly estimating 3D human pose and body shape from a monocular RGB image.
Ranked #82 on
3D Human Pose Estimation
on MPI-INF-3DHP
(using extra training data)
DeepWrinkles: Accurate and Realistic Clothing Modeling
We present a novel method to generate accurate and realistic clothing deformation from real data capture.
Pano2CAD: Room Layout From A Single Panorama Image
This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image.
Timing-Based Local Descriptor for Dynamic Surfaces
In this paper, we present the first local descriptor designed for dynamic surfaces.
Intrinsic Characterization of Dynamic Surfaces
This paper presents a novel approach to characterize deformable surface using intrinsic property dynamics.
