Search Results for author:
Found 52 papers, 26 papers with code
What is Learned in Deep Uncalibrated Photometric Stereo?
Based on this insight, we propose a guided calibration network, named GCNet, that explicitly leverages object shape and shading information for improved lighting estimation.
Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers
Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress in text-driven visual generation.
SPC: Evolving Self-Play Critic via Adversarial Games for LLM Reasoning
Evaluating the step-by-step reliability of large language model (LLM) reasoning, such as Chain-of-Thought, remains challenging due to the difficulty and cost of obtaining high-quality step-level supervision.
VipDiff: Towards Coherent and Diverse Video Inpainting via Training-free Denoising Diffusion Models
Recently, diffusion models have demonstrated impressive performance in generating diverse and high-quality images, and have been exploited in a number of works for image inpainting.
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving
Multimodal large language models (MLLMs) possess the ability to comprehend visual images or videos, and show impressive reasoning ability thanks to the vast amounts of pretrained knowledge, making them highly suitable for autonomous driving applications.
FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality
Our key contributions include a dynamic feature reuse strategy that preserves both feature distinction and temporal continuity, and CFG-Cache which optimizes the reuse of conditional and unconditional outputs to further enhance inference speed without compromising video quality.
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities
We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation capabilities.
AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animation
2) For the ''how'' challenge, we introduce correspondence-aware motion optimization that constructs motion fields for both human and object models using the linear blend skinning function from SMPL-X.
Human-Object Interaction Detection
Human-Object Interaction Generation
+2
ArtiFade: Learning to Generate High-quality Subject from Blemished Images
In this paper, we introduce ArtiFade to tackle this issue and successfully generate high-quality artifact-free images from blemished datasets.
ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction
To achieve this, we present ConceptExpress that tackles UCE by unleashing the inherent capabilities of pretrained diffusion models in two aspects.
Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation
LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) task.
A Survey on 3D Human Avatar Modeling -- From Reconstruction to Generation
3D modeling has long been an important area in computer vision and computer graphics.
Bridging Different Language Models and Generative Vision Models for Text-to-Image Generation
In this paper, we explore this objective and propose LaVi-Bridge, a pipeline that enables the integration of diverse pre-trained language models and generative vision models for text-to-image generation.
PLACE: Adaptive Layout-Semantic Fusion for Semantic Image Synthesis
Extensive experiments demonstrate that our approach performs favorably in terms of visual quality, semantic consistency, and layout alignment.
MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation
Embodied agents equipped with GPT as their brains have exhibited extraordinary decision-making and generalization abilities across various tasks.
DiffusionMat: Alpha Matting as Sequential Refinement Learning
In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes.
Guide3D: Create 3D Avatars from Text and Image Guidance
To this end, we introduce Guide3D, a zero-shot text-and-image-guided generative model for 3D avatar generation based on diffusion models.
RIGID: Recurrent GAN Inversion and Editing of Real Face Videos
In this paper, we propose a unified recurrent framework, named \textbf{R}ecurrent v\textbf{I}deo \textbf{G}AN \textbf{I}nversion and e\textbf{D}iting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos.
Semi-supervised Cycle-GAN for face photo-sketch translation in the wild
Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.
ViCo: Plug-and-play Visual Condition for Personalized Text-to-image Generation
Personalized text-to-image generation using diffusion models has recently emerged and garnered significant interest.
Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models
Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions.
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery
GCD considers the open-world problem of automatically clustering a partially labelled dataset, in which the unlabelled data may contain instances from both novel categories and labelled classes.
DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models
We present DreamAvatar, a text-and-shape guided framework for generating high-quality 3D human avatars with controllable poses.
SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction
We propose a flexible framework which, by leveraging the parametric SMPL-X model, can take an arbitrary number of input images to reconstruct a clothed human model under an uncalibrated setting.
Learning Attention as Disentangler for Compositional Zero-shot Learning
The key to CZSL is learning the disentanglement of the attribute-object composition.
S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
Different from existing single-view methods which can only recover a 2. 5D scene representation (i. e., a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene.
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo
It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer.
JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction
In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach.
A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space.
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel.
Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction
We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset
Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.
Fixed Viewpoint Mirror Surface Reconstruction under an Uncalibrated Camera
This paper addresses the problem of mirror surface reconstruction, and proposes a solution based on observing the reflections of a moving reference plane on the mirror surface.
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature.
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs.
Ranked #5 on
Blind Face Restoration
on CelebA-Test
Deep Variational Network Toward Blind Image Restoration
In this proposed model, a pixel-wise non-i. i. d.
Deep Photometric Stereo for Non-Lambertian Surfaces
To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images.
Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension
To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features.
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video
In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video.
Learning Transparent Object Matting
In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow.
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video
In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video.
Self-calibrating Deep Photometric Stereo Networks
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning.
SAFE: Scale Aware Feature Encoder for Scene Text Recognition
We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales.
Semi-Supervised Learning for Face Sketch Synthesis in the Wild
Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.
Ranked #1 on
Face Sketch Synthesis
on CUHK
PS-FCN: A Flexible Learning Framework for Photometric Stereo
This paper addresses the problem of photometric stereo for non-Lambertian surfaces.
Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition
Unlike previous work which employed a global spatial transformer network to rectify the entire distorted text image, we take an approach of detecting and rectifying individual characters.
TOM-Net: Learning Transparent Object Matting from a Single Image
In this paper, we first formulate transparent object matting as a refractive flow estimation problem.
SCNet: Learning Semantic Correspondence
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category.
Mirror Surface Reconstruction Under an Uncalibrated Camera
Unlike previous approaches which require tedious work to calibrate the camera, our method can recover both the camera intrinsics and extrinsics together with the mirror surface from reflections of the reference plane under at least three unknown distinct poses.
A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects
In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light.
