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20182024
most citedA Review on Deep Learning Techniques for Video Prediction

312 citations · 340 across the 9 of their papers we have counts for

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9 papers · 1 filter

cs.CV2024★ 14 cited

Cafca: High-quality Novel View Synthesis of Expressive Faces from Casual Few-shot Captures

Marcel C. Bühler, Gengyan Li, Erroll Wood +13

Volumetric modeling and neural radiance field representations have revolutionized 3D face capture and photorealistic novel view synthesis. However, these methods often require hund…

cs.CV2023

Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis

Marcel C. Bühler, Kripasindhu Sarkar, Tanmay Shah +8

NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin. These methods typically require a large number o…

cs.CV2023

Controllable Light Diffusion for Portraits

David Futschik, Kelvin Ritland, James Vecore +5

We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumination. Inspired…

cs.CV2023★ 1 cited

Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos

Ziqian Bai, Feitong Tan, Zeng Huang +12

We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achie…

cs.CV2022

VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting

Feitong Tan, Sean Fanello, Abhimitra Meka +6

We propose VoLux-GAN, a generative framework to synthesize 3D-aware faces with convincing relighting. Our main contribution is a volumetric HDRI relighting method that can efficien…

cs.CV2021★ 10 cited

NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis

Zuria Bauer, Zuoyue Li, Sergio Orts-Escolano +3

Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation. In particular, we propose a novel training method split…

cs.CV2021★ 1 cited

UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments

Pablo Martinez-Gonzalez, Sergiu Oprea, John Alejandro Castro-Vargas +4

Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision…

cs.CV2021

H-GAN: the power of GANs in your Hands

Sergiu Oprea, Giorgos Karvounas, Pablo Martinez-Gonzalez +7

We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach implementing multi-scale perceptual discriminators. It is designed to translate synthetic images of han…

cs.CV2020★ 312 cited

A Review on Deep Learning Techniques for Video Prediction

Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia +4

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer…