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Found 14 papers, 1 papers with code
Topo4D: Topology-Preserving Gaussian Splatting for High-Fidelity 4D Head Capture
To simplify this process, we propose Topo4D, a novel framework for automatic geometry and texture generation, which optimizes densely aligned 4D heads and 8K texture maps directly from calibrated multi-view time-series images.
Monocular Identity-Conditioned Facial Reflectance Reconstruction
We first learn a high-quality prior for facial reflectance.
ReGenNet: Towards Human Action-Reaction Synthesis
Humans constantly interact with their surrounding environments.
3D-Aware Face Editing via Warping-Guided Latent Direction Learning
To address the problem of distortion caused by tri-plane warping we train a warp-aware encoder to project the warped face onto a standardized latent space.
Directional Texture Editing for 3D Models
However, the inherent complexity of 3D models and the ambiguous text description lead to the challenge in this task.
HyperStyle3D: Text-Guided 3D Portrait Stylization via Hypernetworks
While the use of 3D-aware GANs bypasses the requirement of 3D data, we further alleviate the necessity of style images with the CLIP model being the stylization guidance.
GANHead: Towards Generative Animatable Neural Head Avatars
To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars.
Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation
Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications.
3D-Aware Face Swapping
To achieve this, we take advantage of the strong geometry and texture prior of 3D human faces, where the 2D faces are projected into the latent space of a 3D generative model.
Deep Neural Network for Blind Visual Quality Assessment of 4K Content
In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality.
Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency
In this paper, we first conduct an example experiment (i. e. the face detection task) to demonstrate that the quality of the SIs has a crucial impact on the performance of the IVSS, and then propose a saliency-based deep neural network for the blind quality assessment of the SIs, which helps IVSS to filter the low-quality SIs and improve the detection and recognition performance.
A No-Reference Deep Learning Quality Assessment Method for Super-resolution Images Based on Frequency Maps
Therefore, in this paper, we propose a no-reference deep-learning image quality assessment method based on frequency maps because the artifacts caused by SISR algorithms are quite sensitive to frequency information.
Parameterized Image Quality Score Distribution Prediction
Experimental results verifythe feasibility of using alpha stable model to describe the IQSD, and prove the effectiveness of objective alpha stable model basedIQSD prediction method.
Terahertz Security Image Quality Assessment by No-reference Model Observers
Subsequently, the existing no-reference IQA algorithms, which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM, CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security image quality.
