2 citations · 4 across the 25 of their papers we have counts for
27 papers
WorldSculpt: Generating Compositional Worlds from Grounded Videos
Muyao Niu, Jixuan He, Ruihan Yu +9
We study the problem of generating a compositional 3D representation of a cluttered scene containing hundreds of objects. The goal is to represent the scene as a collection of indi…
SafeRI: Recognition and Intervention for Token-Level Safety Intervention in Large Vision Language Models
Caoyuan Ma, Tian Gu, Wenpu Liu +11
Existing safety alignment methods for vision-language models usually modify the model behavior globally: once the safety parameters are trained or loaded, they participate in both…
Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation
Guixu Lin, Yuyang Yu, Xiang Ji +6
Latent diffusion models have recently advanced video frame interpolation by synthesizing intermediate frames between input images. However, handling large temporal gaps and complex…
SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning
Caoyuan Ma, Wenpu Liu, Weichu Xie +12
Large vision-language models (LVLMs) remain vulnerable to jailbreak attacks that exploit visual inputs to bypass safety alignment inherited from their language backbones. We propos…
Optical Flow from Photons
Wendi Liu, Weichao Zeng, Weihang Ran +2
Optical flow remains challenging in high-speed and low-light scenes, where the limited frame rate and sensitivity of conventional cameras lead to motion blur and underexposure. Sin…
Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark
Muyao Niu, Mingze Ma, Yifan Zhan +5
Robust low-light imaging remains challenging for the community. Recent studies have explored fusing Near-Infrared (NIR) with noisy RGB to achieve improved enhancement, yet most met…
Surprise Forcing: What to Remember, When to Skip in Long Video Generation
Shuwei Shi, Zhen Li, Muyao Niu +4
Streaming autoregressive diffusion makes minute-scale video synthesis practical, but its bounded context and fixed denoising schedule allocate resources uniformly across a highly n…
MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts
Jiancheng Zhao, Xiang Ji, Yifan Zhan +2
Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existing approaches either adopt task-specific end-to-end designs, raising pa…
Moment-Reenacting: Inverse Motion Degradation with Cross-shutter Guidance
Xiang Ji, Guixu Lin, Zhengwei Yin +2
Motion degradation, manifested as blur in global shutter (GS) images or rolling shutter (RS) distortion in RS counterparts, remains a fundamental challenge in computational imaging…
HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation
Conglang Zhang, Yifan Zhan, Qingjie Wang +10
Closed-loop driving simulation requires real-time interaction beyond short offline clips, pushing current driving world models toward autoregressive (AR) rollout. Existing AR disti…
Composing Driving Worlds through Disentangled Control for Adversarial Scenario Generation
Yifan Zhan, Zhengqing Chen, Qingjie Wang +7
A major challenge in autonomous driving is the "long tail" of safety-critical edge cases, which often emerge from unusual combinations of common traffic elements. Synthesizing thes…
AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models
Muyao Niu, Mingdeng Cao, Yifan Zhan +6
Recent advances in video diffusion models have substantially enhanced character animation techniques. However, existing methods primarily depend on structural conditions, such as D…
Tree-NeRV: A Tree-Structured Neural Representation for Efficient Non-Uniform Video Encoding
Jiancheng Zhao, Yifan Zhan, Qingtian Zhu +5
Implicit Neural Representations for Videos (NeRV) have emerged as a powerful paradigm for video representation, enabling direct mappings from frame indices to video frames. However…
All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception
Jiancheng Zhao, Xiang Ji, Yinqiang Zheng
Efficiently transferring Learned Image Compression (LIC) model from human perception to machine perception is an emerging challenge in vision-centric representation learning. Exist…
R3-Avatar: Record and Retrieve Temporal Codebook for Reconstructing Photorealistic Human Avatars
Yifan Zhan, Wangze Xu, Qingtian Zhu +6
We present R3-Avatar, incorporating a temporal codebook, to overcome the inability of human avatars to be both animatable and of high-fidelity rendering quality. Existing video-bas…
Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach
Guixu Lin, Muyao Niu, Qingtian Zhu +4
Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused o…
Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems
Muyao Niu, Zhuoxiao Li, Yifan Zhan +3
Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut f…
SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics
Qingtian Zhu, Yumin Zheng, Yuling Sang +4
Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distribution and the super-high dimensional s…
Motion-Aware Animatable Gaussian Avatars Deblurring
Muyao Niu, Yifan Zhan, Qingtian Zhu +5
The creation of 3D human avatars from multi-view videos is a significant yet challenging task in computer vision. However, existing techniques rely on high-quality, sharp images as…
ToMiE: Towards Explicit Exoskeleton for the Reconstruction of Complicated 3D Human Avatars
Yifan Zhan, Qingtian Zhu, Muyao Niu +6
In this paper, we highlight a critical yet often overlooked factor in most 3D human tasks, namely modeling complicated 3D human with with hand-held objects or loose-fitting clothin…
EventHDR: from Event to High-Speed HDR Videos and Beyond
Yunhao Zou, Ying Fu, Tsuyoshi Takatani +1
Event cameras are innovative neuromorphic sensors that asynchronously capture the scene dynamics. Due to the event-triggering mechanism, such cameras record event streams with much…
KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter
Yifan Zhan, Zhuoxiao Li, Muyao Niu +4
We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea i…
RS-NeRF: Neural Radiance Fields from Rolling Shutter Images
Muyao Niu, Tong Chen, Yifan Zhan +3
Neural Radiance Fields (NeRFs) have become increasingly popular because of their impressive ability for novel view synthesis. However, their effectiveness is hindered by the Rollin…
MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model
Muyao Niu, Xiaodong Cun, Xintao Wang +3
We present MOFA-Video, an advanced controllable image animation method that generates video from the given image using various additional controllable signals (such as human landma…