12 citations · 36 across the 17 of their papers we have counts for
19 papers
ASTRA: Asynchronous Spatio-Temporal Reconstruction via Trajectory Alignment
Junyu Zhu, Hao Zhu, Xinzhuo Zhang +4
Dynamic 3D scene reconstruction has made significant progress with multi-camera systems, often relying on temporally aligned observations across views. However, in real-world scena…
ReX-Shot: Single-Image Rephotography via Geometry- and Camera-Grounded Generation
Ruiqi Zhang, Hao Zhu, Wenhao Zhang +5
Single-image rephotography aims to synthesize new shots of a scene from a single reference image with specified viewpoints, focal lengths, and photographic effects, which are intri…
Split-Layer: Enhancing Implicit Neural Representation by Maximizing the Dimensionality of Feature Space
Zhicheng Cai, Hao Zhu, Linsen Chen +2
Implicit neural representation (INR) models signals as continuous functions using neural networks, offering efficient and differentiable optimization for inverse problems across di…
WIPES: Wavelet-based Visual Primitives
Wenhao Zhang, Hao Zhu, Delong Wu +4
Pursuing a continuous visual representation that offers flexible frequency modulation and fast rendering speed has recently garnered increasing attention in the fields of 3D vision…
Mitigating Ambiguities in 3D Classification with Gaussian Splatting
Ruiqi Zhang, Hao Zhu, Jingyi Zhao +3
3D classification with point cloud input is a fundamental problem in 3D vision. However, due to the discrete nature and the insufficient material description of point cloud represe…
FINER++: Building a Family of Variable-periodic Functions for Activating Implicit Neural Representation
Hao Zhu, Zhen Liu, Qi Zhang +5
Implicit Neural Representation (INR), which utilizes a neural network to map coordinate inputs to corresponding attributes, is causing a revolution in the field of signal processin…
Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks
Zhicheng Cai, Hao Zhu, Qiu Shen +2
Representing signals using coordinate networks dominates the area of inverse problems recently, and is widely applied in various scientific computing tasks. Still, there exists an…
Neural Poisson Solver: A Universal and Continuous Framework for Natural Signal Blending
Delong Wu, Hao Zhu, Qi Zhang +3
Implicit Neural Representation (INR) has become a popular method for representing visual signals (e.g., 2D images and 3D scenes), demonstrating promising results in various downstr…
FINER: Flexible spectral-bias tuning in Implicit NEural Representation by Variable-periodic Activation Functions
Zhen Liu, Hao Zhu, Qi Zhang +5
Implicit Neural Representation (INR), which utilizes a neural network to map coordinate inputs to corresponding attributes, is causing a revolution in the field of signal processin…
RHINO: Regularizing the Hash-based Implicit Neural Representation
Hao Zhu, Fengyi Liu, Qi Zhang +2
The use of Implicit Neural Representation (INR) through a hash-table has demonstrated impressive effectiveness and efficiency in characterizing intricate signals. However, current…
Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail
Yiyu Zhuang, Qi Zhang, Ying Feng +6
We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail recovery and anti-aliased novel view rendering. Drawing inspiration from voxel-based repr…
AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
Yifei Zeng, Yuanxun Lu, Xinya Ji +3
We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars b…
High-Fidelity 3D Face Generation from Natural Language Descriptions
Menghua Wu, Hao Zhu, Linjia Huang +3
Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. Ho…
NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
Yiyu Zhuang, Qi Zhang, Xuan Wang +5
Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lig…
Disorder-invariant Implicit Neural Representation
Hao Zhu, Shaowen Xie, Zhen Liu +6
Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problem…
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs
Longwei Guo, Hao Zhu, Yuanxun Lu +2
We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap s…
Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
Kailai Zhou, Yibo Wang, Tao Lv +4
We endeavor on a rarely explored task named Insubstantial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indisti…
EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model
Xinya Ji, Hang Zhou, Kaisiyuan Wang +4
Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In t…
FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction
Hao Zhu, Haotian Yang, Longwei Guo +7
In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceSc…