1 citations · 2 across the 14 of their papers we have counts for
14 papers
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…
MindVoice: Reconstructing Intelligible Speech from Non-invasive Neural Signals with Pretrained Priors
Guangyin Bao, Taiping Zeng, Jianfeng Feng +1
Reconstructing continuous speech from non-invasive neural recordings is a fundamental problem for probing human auditory perception and building safe, scalable speech brain-compute…
Conformal Reliability: A New Evaluation Metric for Conditional Generation
Yachen Gao, Xinwei Sun, Yikai Wang +4
Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which ta…
The Pictorial Cortex: Zero-Shot Cross-Subject fMRI-to-Image Reconstruction via Compositional Latent Modeling
Jingyang Huo, Yikai Wang, Yanwei Fu +1
Decoding visual experiences from human brain activity remains a central challenge at the intersection of neuroscience, neuroimaging, and artificial intelligence. A critical obstacl…
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…
DecoFuse: Decomposing and Fusing the "What", "Where", and "How" for Brain-Inspired fMRI-to-Video Decoding
Chong Li, Jingyang Huo, Weikang Gong +3
Decoding visual experiences from brain activity is a significant challenge. Existing fMRI-to-video methods often focus on semantic content while overlooking spatial and motion info…
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…
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…
Brain3D: Generating 3D Objects from fMRI
Yuankun Yang, Li Zhang, Ziyang Xie +4
Understanding the hidden mechanisms behind human's visual perception is a fundamental question in neuroscience. To that end, investigating into the neural responses of human mind a…
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation
Jingyang Huo, Yikai Wang, Xuelin Qian +4
Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality…
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…
fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding
Xuelin Qian, Yun Wang, Jingyang Huo +2
The exploration of brain activity and its decoding from fMRI data has been a longstanding pursuit, driven by its potential applications in brain-computer interfaces, medical diagno…