49 citations · 111 across the 20 of their papers we have counts for
20 papers
Guide3D: Create 3D Avatars from Text and Image Guidance
Yukang Cao, Yan-Pei Cao, Kai Han +2
Recently, text-to-image generation has exhibited remarkable advancements, with the ability to produce visually impressive results. In contrast, text-to-3D generation has not yet re…
Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views
Zi-Xin Zou, Weihao Cheng, Yan-Pei Cao +3
Reconstructing 3D objects from extremely sparse views is a long-standing and challenging problem. While recent techniques employ image diffusion models for generating plausible ima…
GET3D--: Learning GET3D from Unconstrained Image Collections
Fanghua Yu, Xintao Wang, Zheyuan Li +3
The demand for efficient 3D model generation techniques has grown exponentially, as manual creation of 3D models is time-consuming and requires specialized expertise. While generat…
NOFA: NeRF-based One-shot Facial Avatar Reconstruction
Wangbo Yu, Yanbo Fan, Yong Zhang +8
3D facial avatar reconstruction has been a significant research topic in computer graphics and computer vision, where photo-realistic rendering and flexible controls over poses and…
Neural Point-based Volumetric Avatar: Surface-guided Neural Points for Efficient and Photorealistic Volumetric Head Avatar
Cong Wang, Di Kang, Yan-Pei Cao +3
Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications. However, exist…
ID-Pose: Sparse-view Camera Pose Estimation by Inverting Diffusion Models
Weihao Cheng, Yan-Pei Cao, Ying Shan
Given sparse views of a 3D object, estimating their camera poses is a long-standing and intractable problem. Toward this goal, we consider harnessing the pre-trained diffusion mode…
PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas
Zheng Chen, Yan-Pei Cao, Yuan-Chen Guo +3
Achieving an immersive experience enabling users to explore virtual environments with six degrees of freedom (6DoF) is essential for various applications such as virtual reality (V…
SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views
Weihao Cheng, Yan-Pei Cao, Ying Shan
We study to generate novel views of indoor scenes given sparse input views. The challenge is to achieve both photorealism and view consistency. We present SparseGNV: a learning fra…
HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video
Jia-Wei Liu, Yan-Pei Cao, Tianyuan Yang +5
We introduce HOSNeRF, a novel 360° free-viewpoint rendering method that reconstructs neural radiance fields for dynamic human-object-scene from a single monocular in-the-wild video…
SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes
Yiming Gao, Yan-Pei Cao, Ying Shan
Online reconstructing and rendering of large-scale indoor scenes is a long-standing challenge. SLAM-based methods can reconstruct 3D scene geometry progressively in real time but c…
DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models
Yukang Cao, Yan-Pei Cao, Kai Han +2
We present DreamAvatar, a text-and-shape guided framework for generating high-quality 3D human avatars with controllable poses. While encouraging results have been reported by rece…
VMesh: Hybrid Volume-Mesh Representation for Efficient View Synthesis
Yuan-Chen Guo, Yan-Pei Cao, Chen Wang +4
With the emergence of neural radiance fields (NeRFs), view synthesis quality has reached an unprecedented level. Compared to traditional mesh-based assets, this volumetric represen…
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions
Hao Ai, Zidong cao, Yan-pei Cao +2
Depth estimation from a monocular 360° image is a burgeoning problem owing to its holistic sensing of a scene. Recently, some methods, \eg, OmniFusion, have applied the tangent pro…
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
Jiale Xu, Xintao Wang, Weihao Cheng +4
Recent CLIP-guided 3D optimization methods, such as DreamFields and PureCLIPNeRF, have achieved impressive results in zero-shot text-to-3D synthesis. However, due to scratch traini…
MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors
Zi-Xin Zou, Shi-Sheng Huang, Yan-Pei Cao +3
High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstru…
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
Jia-Wei Liu, Yan-Pei Cao, Weijia Mao +6
Modeling dynamic scenes is important for many applications such as virtual reality and telepresence. Despite achieving unprecedented fidelity for novel view synthesis in dynamic sc…
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
Peng Xiang, Xin Wen, Yu-Shen Liu +4
Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point clou…
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering
Ruizhi Shao, Hongwen Zhang, He Zhang +4
We introduce DoubleField, a novel framework combining the merits of both surface field and radiance field for high-fidelity human reconstruction and rendering. Within DoubleField,…
Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding
Xin Wen, Zhizhong Han, Yan-Pei Cao +3
In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired comp…
PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths
Xin Wen, Peng Xiang, Zhizhong Han +4
The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one…