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Found 18 papers, 9 papers with code
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization
We propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles.
OrthoGAN:High-Precision Image Generation for Teeth Orthodontic Visualization
We design a multi-modal encoder-decoder based generative model to synthesize identity-preserving frontal facial images with aligned teeth.
NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations
To improve the efficiency of a traditional hair growth algorithm, we adopt a local neural implicit function to grow strands based on the estimated 3D hair geometric features.
NeuralReshaper: Single-image Human-body Retouching with Deep Neural Networks
In this paper, we present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks.
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.
ADeLA: Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation
We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views.
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot.
SketchHairSalon: Deep Sketch-based Hair Image Synthesis
At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte.
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels in the target domain.
DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis
We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth.
AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
We base our work on the assumption that most human-made objects are constituted by parts and these parts can be well represented by generalized primitives.
SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks
We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches.
SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches.
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.
DeepSketchHair: Deep Sketch-based 3D Hair Modeling
The key enablers of our system are two carefully designed neural networks, namely, S2ONet, which converts an input sketch to a dense 2D hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D vector field.
Graphics
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
To bridge the gap between these two spaces in neural networks, we propose a neural line rasterization module to convert the vector sketch along with the attention estimated by RNN into a bitmap image, which is subsequently consumed by CNN.
Hair-GANs: Recovering 3D Hair Structure from a Single Image
We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image.
Graphics
Recovering 3D Planar Arrangements from Videos
Acquiring 3D geometry of real world objects has various applications in 3D digitization, such as navigation and content generation in virtual environments.
