298 citations · 1.5k across the 72 of their papers we have counts for
75 papers
RegVGGT: Sustainable Visual Geometry Grounding for Streaming via Regulated Memory
Hongbo Mao, Junjun Jiang, Youyu Chen +3
3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference context cannot be retained under l…
S3VD: Semantic-Guidance Spatio-Temporal Scanning for Video Deraining
Kui Jiang, Yiang Chen, Yan Luo +3
Heavy rainfall severely degrades outdoor videos by corrupting high-frequency details and introducing motion blur, critically undermining the reliability of visual tasks. Recently,…
Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning
Yuanchao Bai, Yifan Zhao, Kai Wang +5
Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression method…
ViP-Rig: Visual-Prompted Controllable Rigging
Zihan Qin, Mingze Sun, Yifan Mao +6
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an i…
RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics
Kui Jiang, Runzhe Li, Zhaocheng Yu +3
Video deraining aims to recover clean visual content from rainy videos for reliable perception under adverse weather. Existing methods mainly rely on RGB sequences and temporal red…
NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Suhang Yao +95
This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this c…
HM-Talker: Hybrid Motion Modeling for High-Fidelity Talking Head Synthesis
Shiyu Liu, Kui Jiang, Junjun Jiang +4
Audio-driven talking head generation faces a fundamental trade-off between personalization and generalization, limiting its practical application. Implicit models often achieve gen…
VDEGaussian: Video Diffusion Enhanced 4D Gaussian Splatting for Dynamic Urban Scenes Modeling
Yuru Xiao, Zihan Lin, Chao Lu +7
Dynamic urban scene modeling is a rapidly evolving area with broad applications. While current approaches leveraging neural radiance fields or Gaussian Splatting have achieved fine…
AFBench: A Large-scale Benchmark for Airfoil Design
Jian Liu, Jianyu Wu, Hairun Xie +8
Data-driven generative models have emerged as promising approaches towards achieving efficient mechanical inverse design. However, due to prohibitively high cost in time and money,…
Spatial Annealing for Efficient Few-shot Neural Rendering
Yuru Xiao, Deming Zhai, Wenbo Zhao +3
Neural Radiance Fields (NeRF) with hybrid representations have shown impressive capabilities for novel view synthesis, delivering high efficiency. Nonetheless, their performance si…
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
Yifan Mao, Ming Li, Jian Liu +7
Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous drivin…
Mesh Denoising Transformer
Wenbo Zhao, Xianming Liu, Deming Zhai +2
Mesh denoising, aimed at removing noise from input meshes while preserving their feature structures, is a practical yet challenging task. Despite the remarkable progress in learnin…
The Third Monocular Depth Estimation Challenge
Jaime Spencer, Fabio Tosi, Matteo Poggi +38
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-…
SDGE: Stereo Guided Depth Estimation for 360 Camera Sets
Jialei Xu, Wei Yin, Dong Gong +2
Depth estimation is a critical technology in autonomous driving, and multi-camera systems are often used to achieve a 360 perception. These 360 camera sets often ha…
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Yuru Xiao, Xianming Liu, Deming Zhai +3
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views…
Exploiting Self-Supervised Constraints in Image Super-Resolution
Gang Wu, Junjun Jiang, Kui Jiang +1
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-…
A Comprehensive Survey on 3D Content Generation
Jian Liu, Xiaoshui Huang, Tianyu Huang +8
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is…
Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
Yifan Mao, Jian Liu, Xianming Liu
Monocular depth estimation is a crucial task in computer vision. While existing methods have shown impressive results under standard conditions, they often face challenges in relia…
REPS: Reconstruction-based Point Cloud Sampling
Guoqing Zhang, Wenbo Zhao, Jian Liu +1
Sampling is widely used in various point cloud tasks as it can effectively reduce resource consumption. Recently, some methods have proposed utilizing neural networks to optimize t…
OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
Yuchen Pan, Junjun Jiang, Kui Jiang +3
Depression Recognition (DR) poses a considerable challenge, especially in the context of the growing concerns surrounding privacy. Traditional automatic diagnosis of DR technology…
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Jialei Xu, Xianming Liu, Junjun Jiang +4
Monocular depth estimation from RGB images plays a pivotal role in 3D vision. However, its accuracy can deteriorate in challenging environments such as nighttime or adverse weather…
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
Hanzhang Wang, Haoran Wang, Jinze Yang +7
The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance…
Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression
Daxin Li, Yuanchao Bai, Kai Wang +2
Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually disple…
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning
Chenyang Wang, Junjun Jiang, Xingyu Hu +2
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, as old data from previous tasks is unavailable when learning a new task. To addre…