3 citations · 7 across the 18 of their papers we have counts for
18 papers
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
Senqiao Yang, Kaichen Zhang, Zhaoyang Jia +20
Standard vision-language models (VLMs) suffer from Moravec's paradox: they excel at complex offline visual reasoning but struggle with simple streaming perception tasks and process…
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Xinjie Zhang, Peng Zhang, Shicheng Zheng +21
Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to…
EvoState: Closed-Loop Visual State Management for Long-Form Video Generation
Xinlei Yin, Xiulian Peng, Xiao Li +2
Multi-shot long-form video generation remains challenging due to identity drift and compounding inconsistencies across shots. Existing methods encode past shots as compressed conte…
Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling
Feier Qin, Xiao Li, Yi Zheng +5
Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet most still remain unable to suppo…
Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models
Zongyu Guo, Jiajun He, Zhaoyang Jia +6
Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixels, latents, or tokens) remain external…
StreamGS: Online Generalizable Gaussian Splatting Reconstruction for Unposed Image Streams
Yang LI, Jinglu Wang, Lei Chu +4
The advent of 3D Gaussian Splatting (3DGS) has advanced 3D scene reconstruction and novel view synthesis. With the growing interest of interactive applications that need immediate…
UVRM: A Scalable 3D Reconstruction Model from Unposed Videos
Shiu-hong Kao, Xiao Li, Jinglu Wang +4
Large Reconstruction Models (LRMs) have recently become a popular method for creating 3D foundational models. Training 3D reconstruction models with 2D visual data traditionally re…
Efficient View Synthesis with Neural Radiance Distribution Field
Yushuang Wu, Xiao Li, Jinglu Wang +3
Recent work on Neural Radiance Fields (NeRF) has demonstrated significant advances in high-quality view synthesis. A major limitation of NeRF is its low rendering efficiency due to…
High-Fidelity and Freely Controllable Talking Head Video Generation
Yue Gao, Yuan Zhou, Jinglu Wang +3
Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and control…
Two-shot Video Object Segmentation
Kun Yan, Xiao Li, Fangyun Wei +4
Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In th…
Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction
Mingfang Zhang, Jinglu Wang, Xiao Li +3
The Multiplane Image (MPI), containing a set of fronto-parallel RGBA layers, is an effective and efficient representation for view synthesis from sparse inputs. Yet, its fixed stru…
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
Aotian Wu, Pan He, Xiao Li +3
Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have w…
Estimating Neural Reflectance Field from Radiance Field using Tree Structures
Xiu Li, Xiao Li, Yan Lu
We present a new method for estimating the Neural Reflectance Field (NReF) of an object from a set of posed multi-view images under unknown lighting. NReF represents 3D geometry an…
Neural Capture of Animatable 3D Human from Monocular Video
Gusi Te, Xiu Li, Xiao Li +3
We present a novel paradigm of building an animatable 3D human representation from a monocular video input, such that it can be rendered in any unseen poses and views. Our method i…
Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus
Xiang Li, Jinglu Wang, Xiaohao Xu +3
Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression. Most existing R-VOS methods have a cri…
Reliable Propagation-Correction Modulation for Video Object Segmentation
Xiaohao Xu, Jinglu Wang, Xiao Li +1
Error propagation is a general but crucial problem in online semi-supervised video object segmentation. We aim to suppress error propagation through a correction mechanism with hig…
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation
Xiang Li, Jinglu Wang, Xiao Li +1
Recently, transformer-based image segmentation methods have achieved notable success against previous solutions. While for video domains, how to effectively model temporal context…
Video Instance Segmentation by Instance Flow Assembly
Xiang Li, Jinglu Wang, Xiao Li +1
Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performance…