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Found 17 papers, 3 papers with code
Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays
The main purpose of this paper is to synthesize realistic face images without occlusions based on the images captured by these cameras.
Identity Preserving Face Completion for Large Ocular Region Occlusion
Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity.
Deep Volumetric Video From Very Sparse Multi-View Performance Capture
We present a deep learning-based volumetric capture approach for performance capture using a passive and highly sparse multi-view capture system.
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
Learning Perspective Undistortion of Portraits
In contrast to the previous state-of-the-art approach, our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model.
Learning Formation of Physically-Based Face Attributes
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering.
Dynamic Facial Asset and Rig Generation from a Single Scan
We also model the joint distribution between identities and expressions, enabling the inference of the full set of personalized blendshapes with dynamic appearances from a single neutral input scan.
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers
Being able to infer 3D structures from 2D images with geometric principles, vanishing points have been a well-recognized concept in 3D vision research.
Task-Generic Hierarchical Human Motion Prior using VAEs
We demonstrate the effectiveness of our hierarchical motion variational autoencoder in a variety of tasks including video-based human pose estimation, motion completion from partial observations, and motion synthesis from sparse key-frames.
Ranked #4 on
Motion Synthesis
on LaFAN1
DisUnknown: Distilling Unknown Factors for Disentanglement Learning
In this paper, we adopt a general setting where all factors that are hard to label or identify are encapsulated as a single unknown factor.
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling
We propose ToFu, Topologically consistent Face from multi-view, a geometry inference framework that can produce topologically consistent meshes across facial identities and expressions using a volumetric representation instead of an explicit underlying 3DMM.
DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points.
Exemplar-based Pattern Synthesis with Implicit Periodic Field Network
In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements.
Texturize a GAN Using a Single Image
Can we customize a deep generative model which can generate images that can match the texture of some given image?
Light Sampling Field and BRDF Representation for Physically-based Neural Rendering
Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection
Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset.
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting
We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes.
