274 citations · 685 across the 22 of their papers we have counts for
24 papers
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +54
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration…
DroneSR: Rethinking Few-shot Thermal Image Super-Resolution from Drone-based Perspective
Zhipeng Weng, Xiaopeng Liu, Ce Liu +3
Although large scale models achieve significant improvements in performance, the overfitting challenge still frequently undermines their generalization ability. In super resolution…
DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration
Yan Chen, Hanlin Shang, Ce Liu +6
Video face restoration faces a critical challenge in maintaining temporal consistency while recovering fine facial details from degraded inputs. This paper presents a novel approac…
Dynamic PDB: A New Dataset and a SE(3) Model Extension by Integrating Dynamic Behaviors and Physical Properties in Protein Structures
Ce Liu, Jun Wang, Zhiqiang Cai +10
Despite significant progress in static protein structure collection and prediction, the dynamic behavior of proteins, one of their most vital characteristics, has been largely over…
AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance
Kaihui Cheng, Ce Liu, Qingkun Su +6
Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical developmen…
Stereo Risk: A Continuous Modeling Approach to Stereo Matching
Ce Liu, Suryansh Kumar, Shuhang Gu +3
We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision. As it is well-known that stereo matching boils down to a p…
Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation
Mingwang Xu, Hui Li, Qingkun Su +6
The field of portrait image animation, driven by speech audio input, has experienced significant advancements in the generation of realistic and dynamic portraits. This research de…
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt +13
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on…
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
Ce Liu, Suryansh Kumar, Shuhang Gu +2
We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using…
MaskGIT: Masked Generative Image Transformer
Huiwen Chang, Han Zhang, Lu Jiang +2
Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative tran…
Pyramid Adversarial Training Improves ViT Performance
Charles Herrmann, Kyle Sargent, Lu Jiang +5
Aggressive data augmentation is a key component of the strong generalization capabilities of Vision Transformer (ViT). One such data augmentation technique is adversarial training…
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition
Mark Boss, Varun Jampani, Raphael Braun +3
Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics. Neural approaches such as NeRF have achieved remarkable s…
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting
Varun Jampani, Huiwen Chang, Kyle Sargent +8
Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve comp…
ViTGAN: Training GANs with Vision Transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang +3
Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. In this paper, we investigate if…
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Gengshan Yang, Deqing Sun, Varun Jampani +6
Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structur…
COMISR: Compression-Informed Video Super-Resolution
Yinxiao Li, Pengchong Jin, Feng Yang +3
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression. However, most videos on the…
AutoFlow: Learning a Better Training Set for Optical Flow
Deqing Sun, Daniel Vlasic, Charles Herrmann +6
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the pro…
DVMark: A Deep Multiscale Framework for Video Watermarking
Xiyang Luo, Yinxiao Li, Huiwen Chang +3
Video watermarking embeds a message into a cover video in an imperceptible manner, which can be retrieved even if the video undergoes certain modifications or distortions. Traditio…
Regularizing Generative Adversarial Networks under Limited Data
Hung-Yu Tseng, Lu Jiang, Ce Liu +2
Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This wor…
Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
Innfarn Yoo, Huiwen Chang, Xiyang Luo +4
Digital watermarking is widely used for copyright protection. Traditional 3D watermarking approaches or commercial software are typically designed to embed messages into 3D meshes,…
NeRD: Neural Reflectance Decomposition from Image Collections
Mark Boss, Raphael Braun, Varun Jampani +3
Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challengin…
Depth Extraction from Video Using Non-parametric Sampling
Kevin Karsch, Ce Liu, Sing Bing Kang
We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods…
Boundless: Generative Adversarial Networks for Image Extension
Piotr Teterwak, Aaron Sarna, Dilip Krishnan +4
Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literat…
Adaptively Learning the Crowd Kernel
Omer Tamuz, Ce Liu, Serge Belongie +2
We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen t…