Search Results for author:
Found 21 papers, 4 papers with code
Garment3DGen: 3D Garment Stylization and Texture Generation
We present a plethora of quantitative and qualitative comparisons on various assets both real and generated and provide use-cases of how one can generate simulation-ready 3D garments.
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.
Geometry Transfer for Stylizing Radiance Fields
Shape and geometric patterns are essential in defining stylistic identity.
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.
DiffAvatar: Simulation-Ready Garment Optimization with Differentiable Simulation
By integrating physical simulation into the optimization loop and accounting for the complex nonlinear behavior of cloth and its intricate interaction with the body, our framework recovers body and garment geometry and extracts important material parameters in a physically plausible way.
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.
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.
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.
On Improving the Generalization of Face Recognition in the Presence of Occlusions
In this paper, we address a key limitation of existing 2D face recognition methods: robustness to occlusions.
Adversarial Representation Learning for Text-to-Image Matching
For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem.
Deep Imbalanced Attribute Classification using Visual Attention Aggregation
For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem.
Curriculum Learning of Visual Attribute Clusters for Multi-Task Classification
In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework.
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation
Incorporating additional knowledge in the learning process can be beneficial for several computer vision and machine learning tasks.
Curriculum Learning for Multi-Task Classification of Visual Attributes
Visual attributes, from simple objects (e. g., backpacks, hats) to soft-biometrics (e. g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification.
Predicting Privileged Information for Height Estimation
In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology.
