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Found 21 papers, 16 papers with code
Evaluating General-Purpose AI with Psychometrics
Comprehensive and accurate evaluation of general-purpose AI systems such as large language models allows for effective mitigation of their risks and deepened understanding of their capabilities.
PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via Denoised Score Distillation
We first propose a novel score function, Denoised Score Distillation (DSD), which directly modifies the SDS by introducing negative gradient components to iteratively correct the gradient direction and generate high-quality textures.
StyleInV: A Temporal Style Modulated Inversion Network for Unconditional Video Generation
In this paper, we introduce a novel motion generator design that uses a learning-based inversion network for GAN.
Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation
The challenges of this task lie in the complexity of the scenes, the unique features of anime style, and the lack of high-quality datasets to bridge the domain gap.
GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image Translation
In this paper, we introduce a novel versatile framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), that improves the quality, applicability and controllability of the existing translation models.
CelebV-Text: A Large-Scale Facial Text-Video Dataset
This paper presents CelebV-Text, a large-scale, diverse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to-video generation tasks.
StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces
Recent advances in face manipulation using StyleGAN have produced impressive results.
VToonify: Controllable High-Resolution Portrait Video Style Transfer
Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency.
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields.
Ranked #1 on
Unconditional Video Generation
on CelebV-HQ
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications.
Unsupervised Image-to-Image Translation with Generative Prior
In this work, we present a novel framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm.
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing
In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing.
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data.
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images.
DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results
This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection.
Focal Frequency Loss for Image Reconstruction and Synthesis
In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further.
TSIT: A Simple and Versatile Framework for Image-to-Image Translation
We introduce a simple and versatile framework for image-to-image translation.
Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach
Stop-and-go traffic poses many challenges to tranportation system, but its formation and mechanism are still under exploration. however, it has been proved that by introducing Connected Automated Vehicles(CAVs) with carefully designed controllers one could dampen the stop-and-go waves in the vehicle fleet.
Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment
Given the aging infrastructure and the anticipated growing number of highway work zones in the United States, it is important to investigate work zone merge control, which is critical for improving work zone safety and capacity.
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
The quality of generated videos outperforms those in existing datasets, validated by user studies.
A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images
In this study, runway pavement images are collected using drone at various heights from the Fitchburg Municipal Airport (FMA) in Massachusetts to evaluate their quality and applicability for crack segmentation, from which an optimal height is determined.
