Search Results for author:
Found 47 papers, 14 papers with code
Multimodal Image Synthesis and Editing: The Generative AI Era
With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years.
GMLight: Lighting Estimation via Geometric Distribution Approximation
This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation.
General Neural Gauge Fields
In this work, we extend this problem to a general paradigm with a taxonomy of discrete \& continuous cases, and develop a learning framework to jointly optimize gauge transformations and neural fields.
AI-Generated Images as Data Source: The Dawn of Synthetic Era
The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data.
StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields
In addition, it transforms the grid features according to the reference style which directly leads to high-quality zero-shot style transfer.
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups.
Weakly Supervised 3D Open-vocabulary Segmentation
Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception and thus a crucial objective in computer vision research.
WaveFill: A Wavelet-based Generation Network for Image Inpainting
This paper presents WaveFill, a wavelet-based inpainting network that decomposes images into multiple frequency bands and fills the missing regions in each frequency band separately and explicitly.
Conditional Directed Graph Convolution for 3D Human Pose Estimation
Based on this representation, we further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses.
Ranked #15 on
3D Human Pose Estimation
on MPI-INF-3DHP
Towards Counterfactual Image Manipulation via CLIP
In addition, we design a simple yet effective scheme that explicitly maps CLIP embeddings (of target text) to the latent space and fuses them with latent codes for effective latent code optimization and accurate editing.
Modulated Contrast for Versatile Image Synthesis
Perceiving the similarity between images has been a long-standing and fundamental problem underlying various visual generation tasks.
Bi-level Feature Alignment for Versatile Image Translation and Manipulation
This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence.
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
Specifically, we design GenCo, a Generative Co-training network that mitigates the discriminator over-fitting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training.
Fourier Document Restoration for Robust Document Dewarping and Recognition
State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth.
Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes
This paper presents a novel image synthesis technique that aims to generate a large amount of annotated scene text images for training accurate and robust scene text detection and recognition models.
Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping
This paper presents a scene text detection technique that exploits bootstrapping and text border semantics for accurate localization of texts in scenes.
Spatial Fusion GAN for Image Synthesis
Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry space but few in both.
ESIR: End-to-end Scene Text Recognition via Iterative Image Rectification
Automated recognition of texts in scenes has been a research challenge for years, largely due to the arbitrary variation of text appearances in perspective distortion, text line curvature, text styles and different types of imaging artifacts.
Scene Text Synthesis for Efficient and Effective Deep Network Training
Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.
Hierarchy Composition GAN for High-fidelity Image Synthesis
Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts.
GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition
Recent adversarial learning research has achieved very impressive progress for modelling cross-domain data shifts in appearance space but its counterpart in modelling cross-domain shifts in geometry space lags far behind.
Spatial-Aware GAN for Unsupervised Person Re-identification
The recent person re-identification research has achieved great success by learning from a large number of labeled person images.
Unsupervised Domain Adaptation
Unsupervised Person Re-Identification
Towards Realistic 3D Embedding via View Alignment
Recent advances in generative adversarial networks (GANs) have achieved great success in automated image composition that generates new images by embedding interested foreground objects into background images automatically.
Adversarial Image Composition with Auxiliary Illumination
State-of-the-art methods strive to harmonize the composed image by adapting the style of foreground objects to be compatible with the background image, whereas the potential shadow of foreground objects within the composed image which is critical to the composition realism is largely neglected.
EMLight: Lighting Estimation via Spherical Distribution Approximation
Motivated by the Earth Mover distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution.
Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting
The 3D pose estimation from a single image is a challenging problem due to depth ambiguity.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
+3
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers
With image-level attention, transformers enable to model long-range dependencies and generate diverse contents with autoregressive modeling of pixel-sequence distributions.
Unbalanced Feature Transport for Exemplar-based Image Translation
In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully.
Sparse Needlets for Lighting Estimation with Spherical Transport Loss
Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting.
Blind Image Super-Resolution via Contrastive Representation Learning
Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis.
Marginal Contrastive Correspondence for Guided Image Generation
We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation.
VMRF: View Matching Neural Radiance Fields
With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images.
Auto-regressive Image Synthesis with Integrated Quantization
Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art.
Latent Multi-Relation Reasoning for GAN-Prior based Image Super-Resolution
In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image.
Regularized Vector Quantization for Tokenized Image Synthesis
The first is a prior distribution regularization which measures the discrepancy between a prior token distribution and the predicted token distribution to avoid codebook collapse and low codebook utilization.
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation
The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models.
Face Transformer: Towards High Fidelity and Accurate Face Swapping
Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces.
POCE: Pose-Controllable Expression Editing
POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately.
Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations
To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence.
WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields
Neural Radiance Field (NeRF) has shown impressive performance in novel view synthesis via implicit scene representation.
Pose-Free Neural Radiance Fields via Implicit Pose Regularization
However, as the pose estimator is trained with only rendered images, the pose estimation is usually biased or inaccurate for real images due to the domain gap between real images and rendered images, leading to poor robustness for the pose estimation of real images and further local minima in joint optimization.
DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields
The generated data is applicable across various computer vision tasks, including video segmentation and 3D point cloud segmentation.
TriHuman : A Real-time and Controllable Tri-plane Representation for Detailed Human Geometry and Appearance Synthesis
Creating controllable, photorealistic, and geometrically detailed digital doubles of real humans solely from video data is a key challenge in Computer Graphics and Vision, especially when real-time performance is required.
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis.
StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting
We introduce StyleGaussian, a novel 3D style transfer technique that allows instant transfer of any image's style to a 3D scene at 10 frames per second (fps).
MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models
However, the method of generating illumination maps has poor generalization performance and parametric models such as Spherical Harmonic (SH) and Spherical Gaussian (SG) fall short in capturing high-frequency or low-frequency components.
