24 citations · 30 across the 14 of their papers we have counts for
14 papers
RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling
Shuhong Liu, Gengjia Chang, Jun Liu +4
Camera sensor RAW data offers intrinsic advantages for object detection, including deeper bit depth, preserved physical information, and freedom from image signal processor (ISP) d…
FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
Shuhong Liu, Xining Ge, Ziteng Cui +8
Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by pixel sampling resolution a…
PersonaGesture: Single-Reference Co-Speech Gesture Personalization for Unseen Speakers
Xiangyue Zhang, Yiyi Cai, Kunhang Li +6
We propose PersonaGesture, a diffusion-based pipeline for single-reference co-speech gesture personalization of unseen speakers. Given target speech and one motion clip from a new…
NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results
Shuhong Liu, Chenyu Bao, Ziteng Cui +103
This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and results. The challenge seeks to…
Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis
Ziteng Cui, Shuhong Liu, Xiaoyu Dong +4
High-quality image acquisition in real-world environments remains challenging due to complex illumination variations and inherent limitations of camera imaging pipelines. These iss…
Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
Shuhong Liu, Xining Ge, Ziying Gu +7
Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts but largely leave stochastic noise unres…
I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions
Shuhong Liu, Lin Gu, Ziteng Cui +2
Participating in efforts to endow generative AI with the 3D physical world perception, we propose I2-NeRF, a novel neural radiance field framework that enhances isometric and isotr…
Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment
Ziteng Cui, Xuangeng Chu, Tatsuya Harada
Capturing high-quality photographs under diverse real-world lighting conditions is challenging, as both natural lighting (e.g., low-light) and camera exposure settings (e.g., expos…
ARTalk: Speech-Driven 3D Head Animation via Autoregressive Model
Xuangeng Chu, Nabarun Goswami, Ziteng Cui +2
Speech-driven 3D facial animation aims to generate realistic lip movements and facial expressions for 3D head models from arbitrary audio clips. Although existing diffusion-based m…
Generalizable and Animatable Gaussian Head Avatar
Xuangeng Chu, Tatsuya Harada
In this paper, we propose Generalizable and Animatable Gaussian head Avatar (GAGAvatar) for one-shot animatable head avatar reconstruction. Existing methods rely on neural radiance…
Real-time High-resolution View Synthesis of Complex Scenes with Explicit 3D Visibility Reasoning
Tiansong Zhou, Yebin Liu, Xuangeng Chu +4
Rendering photo-realistic novel-view images of complex scenes has been a long-standing challenge in computer graphics. In recent years, great research progress has been made on enh…
GPAvatar: Generalizable and Precise Head Avatar from Image(s)
Xuangeng Chu, Yu Li, Ailing Zeng +4
Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision…
Audio-Driven 3D Facial Animation from In-the-Wild Videos
Liying Lu, Tianke Zhang, Yunfei Liu +2
Given an arbitrary audio clip, audio-driven 3D facial animation aims to generate lifelike lip motions and facial expressions for a 3D head. Existing methods typically rely on train…
Detection in Crowded Scenes: One Proposal, Multiple Predictions
Xuangeng Chu, Anlin Zheng, Xiangyu Zhang +1
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes. The key of our approach is to let each proposal…