Search Results for author:
Found 22 papers, 5 papers with code
WordRobe: Text-Guided Generation of Textured 3D Garments
We achieve this by first learning a latent representation of 3D garments using a novel coarse-to-fine training strategy and a loss for latent disentanglement, promoting better latent interpolation.
Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting
We propose a novel self-supervised framework for retargeting non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and poses, enabling 3D virtual try-on (VTON).
MANUS: Markerless Grasp Capture using Articulated 3D Gaussians
Since our representation uses Gaussian primitives, it enables us to efficiently and accurately estimate contacts between the hand and the object.
Enhanced Spatio-Temporal Context for Temporally Consistent Robust 3D Human Motion Recovery from Monocular Videos
Recovering temporally consistent 3D human body pose, shape and motion from a monocular video is a challenging task due to (self-)occlusions, poor lighting conditions, complex articulated body poses, depth ambiguity, and limited availability of annotated data.
Facial De-occlusion Network for Virtual Telepresence Systems
Virtual presence is a promising direction in communication and recreation for the future.
xCloth: Extracting Template-free Textured 3D Clothes from a Monocular Image
Existing approaches for 3D garment reconstruction either assume a predefined template for the garment geometry (restricting them to fixed clothing styles) or yield vertex colored meshes (lacking high-frequency textural details).
N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections
Following a preliminary pruning step, N2NSkip connections are randomly added between individual neurons/channels of the pruned network, while maintaining the overall sparsity of the network.
SHARP: Shape-Aware Reconstruction of People in Loose Clothing
The parametric body prior enforces geometrical consistency on the body shape and pose, while the non-parametric representation models loose clothing and handle self-occlusions as well.
Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation
Our framework is an example-based method that attempts to overcome the limitations of existing methods by learning a latent space from a real-world terrain dataset.
Attention based Occlusion Removal for Hybrid Telepresence Systems
Traditionally, video conferencing is a widely adopted solution for telecommunication, but a lack of immersiveness comes inherently due to the 2D nature of facial representation.
Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems
Subsequently, we use these landmarks to perform Thin-Plate-Spline-based texture transfer on UV map panels.
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching
We discuss solutions for the exact and inexact graph isomorphism problems and recall the main spectral properties of the combinatorial graph Laplacian; We provide a novel analysis of the commute-time embedding that allows us to interpret the latter in terms of the PCA of a graph, and to select the appropriate dimension of the associated embedded metric space; We derive a unit hyper-sphere normalization for the commute-time embedding that allows us to register two shapes with different samplings; We propose a novel method to find the eigenvalue-eigenvector ordering and the eigenvector signs using the eigensignature (histogram) which is invariant to the isometric shape deformations and fits well in the spectral graph matching framework, and we present a probabilistic shape matching formulation using an expectation maximization point registration algorithm which alternates between aligning the eigenbases and finding a vertex-to-vertex assignment.
End-to-End High Dynamic Range Camera Pipeline Optimization
Traditionally, hardware ISP settings used by downstream vision modules have been chosen by domain experts.
Reconstruction of People in Loose Clothing
3D human body reconstruction from monocular images is an interesting and ill-posed problem in computer vision with wider applications in multiple domains.
GlocalNet: Class-aware Long-term Human Motion Synthesis
Synthesis of long-term human motion skeleton sequences is essential to aid human-centric video generation with potential applications in Augmented Reality, 3D character animations, pedestrian trajectory prediction, etc.
AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few.
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
High resolution Digital Elevation Models(DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc.
Action Quality Assessment using Siamese Network-Based Deep Metric Learning
the reference video and map those variations to the final score.
PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction
We introduce PeeledHuman - a novel shape representation of the human body that is robust to self-occlusions.
HumanMeshNet: Polygonal Mesh Recovery of Humans
3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc.
DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences
3D Human Motion Indexing and Retrieval is an interesting problem due to the rise of several data-driven applications aimed at analyzing and/or re-utilizing 3D human skeletal data, such as data-driven animation, analysis of sports bio-mechanics, human surveillance etc.
Deep Textured 3D Reconstruction of Human Bodies
In this paper, we propose a deep learning based solution for textured 3D reconstruction of human body shapes from a single view RGB image.
