Search Results for author:
Found 22 papers, 14 papers with code
SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer
Recent advances in 2D/3D generative models enable the generation of dynamic 3D objects from a single-view video.
Repositioning the Subject within Image
Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task.
MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo
Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms.
Ranked #1 on
Point Clouds
on Tanks and Temples
Towards Context-Stable and Visual-Consistent Image Inpainting
Recent progress in inpainting increasingly relies on generative models, leveraging their strong generation capabilities for addressing large irregular masks.
Local Consensus Enhanced Siamese Network with Reciprocal Loss for Two-view Correspondence Learning
First, we propose a Local Feature Consensus (LFC) plugin block to augment the features of existing models.
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion Model
As an exemplar, we leverage LeftRefill to address two different challenges: reference-guided inpainting and novel view synthesis, based on the pre-trained StableDiffusion.
Rethinking Optical Flow from Geometric Matching Consistent Perspective
In this paper, we propose a rethinking to previous optical flow estimation.
Rethinking the Multi-view Stereo from the Perspective of Rendering-based Augmentation
To address these problems, we first apply one of the state-of-the-art learning-based MVS methods, --MVSFormer, to overcome intractable scenarios such as textureless and reflections regions suffered by traditional PatchMatch methods, but it fails in a few large scenes' reconstructions.
Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints
Learning robust local image feature matching is a fundamental low-level vision task, which has been widely explored in the past few years.
ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors
Specifically, given one corrupt image, we present the Transformer Structure Restorer (TSR) module to restore holistic structural priors at low image resolution, which are further upsampled by Simple Structure Upsampler (SSU) module to higher image resolution.
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth
In this paper, we propose a pre-trained ViT enhanced MVS network called MVSFormer, which can learn more reliable feature representations benefited by informative priors from ViT.
Ranked #2 on
3D Reconstruction
on DTU
Learning Prior Feature and Attention Enhanced Image Inpainting
To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process.
Wavelet Prior Attention Learning in Axial Inpainting Network
Image inpainting is the task of filling masked or unknown regions of an image with visually realistic contents, which has been remarkably improved by Deep Neural Networks (DNNs) recently.
Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images
We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses.
Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding
The proposed model restores holistic image structures with a powerful attention-based transformer model in a fixed low-resolution sketch space.
The Image Local Autoregressive Transformer
To address these limitations, we propose a novel model -- image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis.
Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes
To this end, this paper proposes learning a Sketch Tensor (ST) space for inpainting man-made scenes.
SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion
However, some studies show that customized self-supervised tasks for a particular type of downstream task can effectively help the pre-trained model to capture more corresponding knowledge and semantic information.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge
The continuous dimensional emotion modelled by arousal and valence can depict complex changes of emotions.
