Search Results for author:
Found 34 papers, 6 papers with code
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF.
NeRF-Editing: Geometry Editing of Neural Radiance Fields
In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network.
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN
The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques.
Socially-Optimal Mechanism Design for Incentivized Online Learning
Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment.
High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling
Instead of decoding a whole shape, we propose to decode and refine a low-resolution (low-res) point cloud first, and then performs a patch-wise noise-aware upsampling rather than interpolating the whole sparse point cloud at once, which tends to lose details.
OctField: Hierarchical Implicit Functions for 3D Modeling
We achieve this goal by introducing a hierarchical octree structure to adaptively subdivide the 3D space according to the surface occupancy and the richness of part geometry.
Multi-sensor joint target detection, tracking and classification via Bernoulli filter
This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion.
Robust Pose Transfer with Dynamic Details using Neural Video Rendering
Moreover, we introduce a concise temporal loss in the training stage to suppress the detail flickering that is made more visible due to high-quality dynamic details generated by our method.
Autoregressive Stylized Motion Synthesis With Generative Flow
Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics.
Deep Deformation Detail Synthesis for Thin Shell Models
Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes.
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning
Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning.
Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales.
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Currently, 3D-FRONT contains 18, 968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets.
TM-NET: Deep Generative Networks for Textured Meshes
We introduce a conditional autoregressive model for texture generation, which can be conditioned on both part geometry and textures already generated for other parts to achieve texture compatibility.
Graphics
RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures.
3D-FUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories.
3D Object Reconstruction From A Single Image
3D Shape Retrieval
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DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structure, in a controllable manner.
Deep Generation of Face Images from Sketches
Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches.
Deep Line Art Video Colorization with a Few References
The color transform network takes the target line art images as well as the line art and color images of one or more reference images as input, and generates corresponding target color images.
STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image
3D reconstruction from a single view image is a long-standing prob-lem in computer vision.
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction
Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations.
A Survey on Deep Geometry Learning: From a Representation Perspective
However, the performance for different applications largely depends on the representation used, and there is no unique representation that works well for all applications.
Graphics
Learning-based Real-time Detection of Intrinsic Reflectional Symmetry
In this paper, we propose \YL{a} learning-based approach to intrinsic reflectional symmetry detection.
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
3D models are commonly used in computer vision and graphics.
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape.
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding
We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space, where the configurations of objects and feature points have approximate one-to-one mapping.
5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion
5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays.
SDM-NET: Deep Generative Network for Structured Deformable Mesh
At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring a coherence between global shape structure and surface details.
Mesh Variational Autoencoders with Edge Contraction Pooling
3D shape analysis is an important research topic in computer vision and graphics.
VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation
Our approach results in a good volumetric representation that effectively tackles noisy point cloud datasets and is more robust for learning.
Graphics
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences
In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs).
Graphics
Variational Autoencoders for Deforming 3D Mesh Models
3D geometric contents are becoming increasingly popular.
Graphics
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing.
Graphics
Sparse Data Driven Mesh Deformation
This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied.
Graphics
