44 citations · 122 across the 20 of their papers we have counts for
15 papers · 1 filter
EG4D: Explicit Generation of 4D Object without Score Distillation
Qi Sun, Zhiyang Guo, Ziyu Wan +5
In recent years, the increasing demand for dynamic 3D assets in design and gaming applications has given rise to powerful generative pipelines capable of synthesizing high-quality…
RaFE: Generative Radiance Fields Restoration
Zhongkai Wu, Ziyu Wan, Jing Zhang +2
NeRF (Neural Radiance Fields) has demonstrated tremendous potential in novel view synthesis and 3D reconstruction, but its performance is sensitive to input image quality, which st…
CAD: Photorealistic 3D Generation via Adversarial Distillation
Ziyu Wan, Despoina Paschalidou, Ian Huang +5
The increased demand for 3D data in AR/VR, robotics and gaming applications, gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of the…
HeadArtist: Text-conditioned 3D Head Generation with Self Score Distillation
Hongyu Liu, Xuan Wang, Ziyu Wan +4
This work presents HeadArtist for 3D head generation from text descriptions. With a landmark-guided ControlNet serving as the generative prior, we come up with an efficient pipelin…
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields
Jingbo Zhang, Xiaoyu Li, Ziyu Wan +2
Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D…
Learning Neural Duplex Radiance Fields for Real-Time View Synthesis
Ziyu Wan, Christian Richardt, Aljaž Božič +8
Neural radiance fields (NeRFs) enable novel view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer pe…
Adaptive Joint Optimization for 3D Reconstruction with Differentiable Rendering
Jingbo Zhang, Ziyu Wan, Jing Liao
Due to inevitable noises introduced during scanning and quantization, 3D reconstruction via RGB-D sensors suffers from errors both in geometry and texture, leading to artifacts suc…
FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
Jingbo Zhang, Xiaoyu Li, Ziyu Wan +2
We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first NeRF-based method capable of reconstruction and expression editing of 3D faces based on a small number of dy…
Bringing Old Films Back to Life
Ziyu Wan, Bo Zhang, Dongdong Chen +1
We present a learning-based framework, recurrent transformer network (RTN), to restore heavily degraded old films. Instead of performing frame-wise restoration, our method is based…
PD-GAN: Probabilistic Diverse GAN for Image Inpainting
Hongyu Liu, Ziyu Wan, Wei Huang +3
We propose PD-GAN, a probabilistic diverse GAN for image inpainting. Given an input image with arbitrary hole regions, PD-GAN produces multiple inpainting results with diverse and…
High-Fidelity Pluralistic Image Completion with Transformers
Ziyu Wan, Jingbo Zhang, Dongdong Chen +1
Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properti…
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
Hongyu Liu, Ziyu Wan, Wei Huang +5
User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and…
Old Photo Restoration via Deep Latent Space Translation
Ziyu Wan, Bo Zhang, Dongdong Chen +4
We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised l…
Bringing Old Photos Back to Life
Ziyu Wan, Bo Zhang, Dongdong Chen +4
We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised l…
Transductive Zero-Shot Learning with Visual Structure Constraint
Ziyu Wan, Dongdong Chen, Yan Li +4
To recognize objects of the unseen classes, most existing Zero-Shot Learning(ZSL) methods first learn a compatible projection function between the common semantic space and the vis…