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VD-NeRF: Visibility-aware Decoupled Neural Radiance Fields for View-Consistent Editing and High-Frequency Relighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Neural Radiance Fields (NeRFs) have shown promising results in novel view synthesis. While achieving state-of-the-art rendering results, NeRF usually encodes all properties related to geometry and appearance of the scene together into several MLP (Multi-Layer Perceptron) networks, which hinders downstream manipulation of geometry, appearance and illumination. Recently researchers made attempts to ...Show More
Towards High-Quality and Disentangled Face Editing in a 3D GAN
Recent methods for synthesizing 3D-aware face images have achieved rapid development thanks to neural radiance fields, allowing for high quality and fast inference speed. However, existing solutions for editing facial geometry and appearance independently usually require retraining and are not optimized for the recent work of generation, thus tending to lag behind the generation process. To addres...Show More
MuNeRF: Robust Makeup Transfer in Neural Radiance Fields
There has been a high demand for facial makeup transfer tools in fashion e-commerce and virtual avatar generation. Most of the existing makeup transfer methods are based on the generative adversarial networks. Despite their success in makeup transfer for a single image, they struggle to maintain the consistency of makeup under different poses and expressions of the same person. In this article, we...Show More
Real-Time 3D-Aware Portrait Video Relighting
Ziqi Cai;Kaiwen Jiang;Shu-Yu Chen;Yu-Kun Lai;Hongbo Fu;Boxin Shi;Lin Gao
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However, most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper, we present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Ra...Show More
Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes
Yujie Lu;Long Want;Nayu Ding;Yulong Wang;Shuhan Shen;Shen Cai;Lin Gao
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However, common distance field based implicit represen-tations, specifically signed distance field (SDF) for water-tight shapes or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and m...Show More
HSDF: Hybrid Sign and Distance Field for Neural Representation of Surfaces with Arbitrary Topologies
Li Wang;Yu-Tao Liu;Jie Yang;Weikai Chen;Xiaoxu Meng;Bo Yang;Jintao Li;Lin Gao
IEEE Transactions on Visualization and Computer Graphics
Year: 2024 | Early Access Article |
Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed surfaces. Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and single-layered open surfaces. Nonetheless, as UDF is signless, its direct output is limited to the...Show More
NeRF-Texture: Synthesizing Neural Radiance Field Textures
Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method ...Show More
Text2Face: Text-Based Face Generation With Geometry and Appearance Control
Zhaoyang Zhang;Junliang Chen;Hongbo Fu;Jianjun Zhao;Shu-Yu Chen;Lin Gao
Recent years have witnessed the emergence of various techniques proposed for text-based human face generation and manipulation. Such methods, targeting bridging the semantic gap between text and visual contents, provide users with a deft hand to turn ideas into visuals via text interface and enable more diversified multimedia applications. However, due to the flexibility of linguistic expressivene...Show More
Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-Made Objects
We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to their limited training samples and thus cannot beautify freely dr...Show More
NeUDF: Learning Neural Unsigned Distance Fields With Volume Rendering
Yu-Tao Liu;Li Wang;Jie Yang;Weikai Chen;Xiaoxu Meng;Bo Yang;Lin Gao
Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in the neural implicit rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUDF, t...Show More
LC-NeRF: Local Controllable Face Generation in Neural Radiance Field
3D face generation has achieved high visual quality and 3D consistency thanks to the development of neural radiance fields (NeRF). However, these methods model the whole face as a neural radiance field, which limits the controllability of the local regions. In other words, previous methods struggle to independently control local regions, such as the mouth, nose, and hair. To improve local controll...Show More
Keyframe Control of Music-Driven 3D Dance Generation
Zhipeng Yang;Yu-Hui Wen;Shu-Yu Chen;Xiao Liu;Yuan Gao;Yong-Jin Liu;Lin Gao;Hongbo Fu
For 3D animators, choreography with artificial intelligence has attracted more attention recently. However, most existing deep learning methods mainly rely on music for dance generation and lack sufficient control over generated dance motions. To address this issue, we introduce the idea of keyframe interpolation for music-driven dance generation and present a novel transition generation technique...Show More
Identity-Aware and Shape-Aware Propagation of Face Editing in Videos
The development of deep generative models has inspired various facial image editing methods, but many of them are difficult to be directly applied to video editing due to various challenges ranging from imposing 3D constraints, preserving identity consistency, ensuring temporal coherence, etc. To address these challenges, we propose a new framework operating on the StyleGAN2 latent space for ident...Show More
SAC-GAN: Structure-Aware Image Composition
Hang Zhou;Rui Ma;Ling-Xiao Zhang;Lin Gao;Ali Mahdavi-Amiri;Hao Zhang
We introduce an end-to-end learning framework for image-to-image composition, aiming to plausibly compose an object represented as a cropped patch from an object image into a background scene image. As our approach emphasizes more on semantic and structural coherence of the composed images, rather than their pixel-level RGB accuracies, we tailor the input and output of our network with structure-a...Show More
Cambricon-R: A Fully Fused Accelerator for Real-Time Learning of Neural Scene Representation
Xinkai Song;Yuanbo Wen;Xing Hu;Tianbo Liu;Yifan Hao;Haoxuan Zhou;Husheng Han;Tian Zhi;Zidong Du;Wei Li;Rui Zhang;Chen Zhang;Lin Gao;Qi Guo;Tianshi Chen
2023 56th IEEE/ACM International Symposium on Microarchitecture (MICRO)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Neural scene representation (NSR) initiates a new methodology of encoding a 3D scene with neural networks by learning from dozens of photos taken from different camera positions. NSR not only achieves significant improvement in the quality of novel view synthesis and 3D reconstruction but also reduces the camera cost from the expensive laser cameras to the cheap color cameras on the shelf. However...Show More
E3Sym: Leveraging E(3) Invariance for Unsupervised 3D Planar Reflective Symmetry Detection
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Detecting symmetrical properties is a fundamental task in 3D shape analysis. In the case of a 3D model with planar symmetries, each point has a corresponding mirror point w.r.t. a symmetry plane, and the correspondences remain invariant under any arbitrary Euclidean transformation. Our proposed method, E3Sym, aims to detect planar reflective symmetry in an unsupervised and end-to-end manner by lev...Show More
Interactive NeRF Geometry Editing With Shape Priors
Yu-Jie Yuan;Yang-Tian Sun;Yu-Kun Lai;Yuewen Ma;Rongfei Jia;Leif Kobbelt;Lin Gao
Neural Radiance Fields (NeRFs) have shown great potential for tasks like novel view synthesis of static 3D scenes. Since NeRFs are trained on a large number of input images, it is not trivial to change their content afterwards. Previous methods to modify NeRFs provide some control but they do not support direct shape deformation which is common for geometry representations like triangle meshes. In...Show More
STAR-TM: STructure Aware Reconstruction of Textured Mesh From Single Image
We present a novel method for single-view 3D reconstruction of textured meshes, with a focus to address the primary challenge surrounding texture inference and transfer. Our key observation is that learning textured reconstruction in a structure-aware and globally consistent manner is effective in handling the severe ill-posedness of the texturing problem and significant variations in object pose ...Show More
NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering
Yu-Tao Liu;Li Wang;Jie Yang;Weikai Chen;Xiaoxu Meng;Bo Yang;Lin Gao
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (22)
Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that contain open-surface structures. In this work, we introduce a new neural rendering framework, coded NeUD...Show More
GLIM-Net: Chronic Glaucoma Forecast Transformer for Irregularly Sampled Sequential Fundus Images
Xiaoyan Hu;Ling-Xiao Zhang;Lin Gao;Weiwei Dai;Xiaoguang Han;Yu-Kun Lai;Yiqiang Chen
Chronic Glaucoma is an eye disease with progressive optic nerve damage. It is the second leading cause of blindness after cataract and the first leading cause of irreversible blindness. Glaucoma forecast can predict future eye state of a patient by analyzing the historical fundus images, which is helpful for early detection and intervention of potential patients and avoiding the outcome of blindne...Show More
SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation With Fine-Grained Geometry
Lin Gao;Jia-Mu Sun;Kaichun Mo;Yu-Kun Lai;Leonidas J. Guibas;Jie Yang
3D indoor scenes are widely used in computer graphics, with applications ranging from interior design to gaming to virtual and augmented reality. They also contain rich information, including room layout, as well as furniture type, geometry, and placement. High-quality 3D indoor scenes are highly demanded while it requires expertise and is time-consuming to design high-quality 3D indoor scenes man...Show More
Neural Radiance Fields From Sparse RGB-D Images for High-Quality View Synthesis
The recently proposed neural radiance fields (NeRF) use a continuous function formulated as a multi-layer perceptron (MLP) to model the appearance and geometry of a 3D scene. This enables realistic synthesis of novel views, even for scenes with view dependent appearance. Many follow-up works have since extended NeRFs in different ways. However, a fundamental restriction of the method remains that ...Show More
Human Motion Transfer With 3D Constraints and Detail Enhancement
Yang-Tian Sun;Qian-Cheng Fu;Yue-Ren Jiang;Zitao Liu;Yu-Kun Lai;Hongbo Fu;Lin Gao
We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target chara...Show More
DrawingInStyles: Portrait Image Generation and Editing With Spatially Conditioned StyleGAN
The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques. The recently proposed StyleGAN architectures achieve state-of-the-art generation ability but the original StyleGAN is not friendly for sketch-based creation due to its unconditional generation nature. To address this issue, we propose a direct conditioning strategy to better preserv...Show More
Learning-Based Intrinsic Reflectional Symmetry Detection
Yi-Ling Qiao;Lin Gao;Shu-Zhi Liu;Ligang Liu;Yu-Kun Lai;Xilin Chen
Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which c...Show More
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