1 citations · 4 across the 17 of their papers we have counts for
19 papers
WorldMemBench: A Comprehensive Benchmark for Memory of Video World Models
Nan Zhou, Jianxiong Gao, Qiuyu Wang +10
As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requires both preserving previousl…
fMRI2Face: A Full-HD fMRI-Video Dataset and Geometry-Guided Neural Decoding Framework for Dynamic Human Face Reconstruction
Jingyang Huo, Xiangru Huang, Chentao Shen +6
Reconstructing dynamic human faces from brain activity provides a powerful way to study how the mind perceives identity, expression, and facial motion. However, progress in fMRI-ba…
MemLearner: Learning to Query Context memory for Video World Models
Jiwen Yu, Jianxiong Gao, Jianhong Bai +7
Video World Models are interactive video generation models that predict future world states based on user actions and history video frames. A critical challenge in video world mode…
What Semantics Survive the Connector? Diagnosing VLM-to-DiT Alignment in Video Editing
Hangyu Lin, Chao Wen, Chengming Xu +4
Flow matching based video generative models have been increasingly relying on prepended Vision-Language Models (VLMs) to handle complex, instruction-based video editing. The prevai…
-WM: A Unified Video-Action World Model for Robotic Manipulation
Pengfei Zhou, Shengcong Chen, Di Chen +17
Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present -World…
Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction
Yujie Wei, Chenglong Ma, Jianxiong Gao +6
Reconstructing dynamic visual experiences as videos from functional magnetic resonance imaging (fMRI) is pivotal for advancing the understanding of neural processes. However, curre…
Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic
Wanying Qu, Jianxiong Gao, Wei Wang +1
Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution…
EchoGen: Cycle-Consistent Learning for Unified Layout-Image Generation and Understanding
Kai Zou, Hongbo Liu, Dian Zheng +3
In this work, we present EchoGen, a unified framework for layout-to-image generation and image grounding, capable of generating images with accurate layouts and high fidelity to te…
LongVie 2: Multimodal Controllable Ultra-Long Video World Model
Jianxiong Gao, Zhaoxi Chen, Xian Liu +7
Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should p…
LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation
Jianxiong Gao, Zhaoxi Chen, Xian Liu +5
Controllable ultra-long video generation is a fundamental yet challenging task. Although existing methods are effective for short clips, they struggle to scale due to issues such a…
CineBrain: A Large-Scale Multi-Modal Brain Dataset During Naturalistic Audiovisual Narrative Processing
Jianxiong Gao, Yichang Liu, Baofeng Yang +2
Most research decoding brain signals into images, often using them as priors for generative models, has focused only on visual content. This overlooks the brain's natural ability t…
Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals
Yanwei Fu, Jianxiong Gao, Baofeng Yang +1
This paper studies the brave new idea for Multimedia community, and proposes a novel framework to convert dreams into coherent video narratives using fMRI data. Essentially, dreams…
Factorized Visual Tokenization and Generation
Zechen Bai, Jianxiong Gao, Ziteng Gao +4
Visual tokenizers are fundamental to image generation. They convert visual data into discrete tokens, enabling transformer-based models to excel at image generation. Despite their…
VideoSAM: Open-World Video Segmentation
Pinxue Guo, Zixu Zhao, Jianxiong Gao +5
Video segmentation is essential for advancing robotics and autonomous driving, particularly in open-world settings where continuous perception and object association across video f…
MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Dataset
Jianxiong Gao, Yanwei Fu, Yuqian Fu +3
Reconstructing 3D visuals from functional Magnetic Resonance Imaging (fMRI) data, introduced as Recon3DMind, is of significant interest to both cognitive neuroscience and computer…
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion
Jinyu Zhang, Yongchong Gu, Jianxiong Gao +5
This paper addresses the challenge of perceiving complete object shapes through visual perception. While prior studies have demonstrated encouraging outcomes in segmenting the visi…
Hyper-Transformer for Amodal Completion
Jianxiong Gao, Xuelin Qian, Longfei Liang +2
Amodal object completion is a complex task that involves predicting the invisible parts of an object based on visible segments and background information. Learning shape priors is…
MinD-3D: Reconstruct High-quality 3D objects in Human Brain
Jianxiong Gao, Yuqian Fu, Yun Wang +3
In this paper, we introduce Recon3DMind, an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals, marking a significant adva…
Coarse-to-Fine Amodal Segmentation with Shape Prior
Jianxiong Gao, Xuelin Qian, Yikai Wang +4
Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to…