HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion

• URL: http://arxiv.org/abs/2510.03122v2
• Date: Sun, 12 Oct 2025 15:32:28 GMT
• Title: HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion
• Abstract summary: The HAVIR model separates the visual cortex into two hierarchical regions and extracts distinct features from each.<n>Results demonstrate that HAVIR enhances both the structural and semantic quality of reconstructions, even in complex scenes.
• Score: 11.802381972880447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reconstruction of visual information from brain activity fosters interdisciplinary integration between neuroscience and computer vision. However, existing methods still face challenges in accurately recovering highly complex visual stimuli. This difficulty stems from the characteristics of natural scenes: low-level features exhibit heterogeneity, while high-level features show semantic entanglement due to contextual overlaps. Inspired by the hierarchical representation theory of the visual cortex, we propose the HAVIR model, which separates the visual cortex into two hierarchical regions and extracts distinct features from each. Specifically, the Structural Generator extracts structural information from spatial processing voxels and converts it into latent diffusion priors, while the Semantic Extractor converts semantic processing voxels into CLIP embeddings. These components are integrated via the Versatile Diffusion model to synthesize the final image. Experimental results demonstrate that HAVIR enhances both the structural and semantic quality of reconstructions, even in complex scenes, and outperforms existing models.

Related papers

• Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering [57.38215918201251]
  We propose HERL, a Hyperbolic Enhanced Representation Learning framework for Incomplete Multi-View Clustering.<n> operating within the Poincaré ball, HERL constructs a structure-aware latent space to enhance representation learning.<n>We show that HERL consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-04-18T10:50:46Z)
• SynMind: Reducing Semantic Hallucination in fMRI-Based Image Reconstruction [52.34513874272676]
  We argue that existing methods rely too heavily on entangled visual embeddings over explicit semantic identity.<n>We parse fMRI signals into rich, sentence-level semantic descriptions that mirror the hierarchical and compositional nature of human visual understanding.<n>We propose SynMind, a framework that integrates these explicit semantic encodings with visual priors to condition a pretrained diffusion model.
  arXiv  Detail & Related papers  (2026-01-25T14:31:23Z)
• StdGEN++: A Comprehensive System for Semantic-Decomposed 3D Character Generation [57.06461272772509]
  StdGEN++ is a novel and comprehensive system for generating high-fidelity, semantically decomposed 3D characters from diverse inputs.<n>It achieves state-of-the-art performance, significantly outperforming existing methods in geometric accuracy and semantic disentanglement.<n>The resulting structural independence unlocks advanced downstream capabilities, including non-destructive editing, physics-compliant animation, and gaze tracking.
  arXiv  Detail & Related papers  (2026-01-12T15:41:27Z)
• InpaintHuman: Reconstructing Occluded Humans with Multi-Scale UV Mapping and Identity-Preserving Diffusion Inpainting [64.42884719282323]
  InpaintHuman is a novel method for generating high-fidelity, complete, and animatable avatars from occluded monocular videos.<n>Our approach employs direct pixel-level supervision to ensure identity fidelity.
  arXiv  Detail & Related papers  (2026-01-05T13:26:02Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• HAVIR: HierArchical Vision to Image Reconstruction using CLIP-Guided Versatile Diffusion [3.9136086794667597]
  Reconstructing visual information from brain activity bridges the gap between neuroscience and computer vision.<n>HAVIR reconstructs both structural features and semantic information of visual stimuli even in complex scenarios.
  arXiv  Detail & Related papers  (2025-06-06T12:33:49Z)
• Manifold-aware Representation Learning for Degradation-agnostic Image Restoration [135.90908995927194]
  Image Restoration (IR) aims to recover high quality images from degraded inputs affected by various corruptions such as noise, blur, haze, rain, and low light conditions.<n>We present MIRAGE, a unified framework for all in one IR that explicitly decomposes the input feature space into three semantically aligned parallel branches.<n>This modular decomposition significantly improves generalization and efficiency across diverse degradations.
  arXiv  Detail & Related papers  (2025-05-24T12:52:10Z)
• ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting [66.29782808719301]
  Building articulated objects is a key challenge in computer vision.<n>Existing methods often fail to effectively integrate information across different object states.<n>We introduce ArtGS, a novel approach that leverages 3D Gaussians as a flexible and efficient representation.
  arXiv  Detail & Related papers  (2025-02-26T10:25:32Z)
• Unpaired Deblurring via Decoupled Diffusion Model [55.21345354747609]
  We propose UID-Diff, a generative-diffusion-based model designed to enhance deblurring performance on unknown domains.<n>We employ two Q-Formers as structural features and blur patterns extractors separately. The features extracted will be used for the supervised deblurring task on synthetic data and the unsupervised blur-transfer task.<n>Experiments on real-world datasets demonstrate that UID-Diff outperforms existing state-of-the-art methods in blur removal and structural preservation.
  arXiv  Detail & Related papers  (2025-02-03T17:00:40Z)
• MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion [7.597218661195779]
  We propose a two-stage image reconstruction model called MindDiffuser. In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings decoded from fMRI are put into Stable Diffusion. In Stage 2, we utilize the CLIP visual feature decoded from fMRI as supervisory information, and continually adjust the two feature vectors decoded in Stage 1 through backpagation to align the structural information.
  arXiv  Detail & Related papers  (2023-08-08T13:28:34Z)
• MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion [8.299415606889024]
  We propose a two-stage image reconstruction model called MindDiffuser. In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings decoded from fMRI are put into the image-to-image process of Stable Diffusion. In Stage 2, we utilize the low-level CLIP visual features decoded from fMRI as supervisory information.
  arXiv  Detail & Related papers  (2023-03-24T16:41:42Z)
• Natural scene reconstruction from fMRI signals using generative latent diffusion [1.90365714903665]
  We present a two-stage scene reconstruction framework called Brain-Diffuser'' In the first stage, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Vari Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model conditioned on predicted multimodal (text and visual) features.
  arXiv  Detail & Related papers  (2023-03-09T15:24:26Z)
• Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution [69.11604249813304]
  We propose a components decomposition and co-optimization network (CDCN) for blind SR. CDCN decomposes the input LR image into structure and detail components in feature space. We present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process.
  arXiv  Detail & Related papers  (2022-12-03T14:53:56Z)
• High-resolution Face Swapping via Latent Semantics Disentanglement [50.23624681222619]
  We present a novel high-resolution hallucination face swapping method using the inherent prior knowledge of a pre-trained GAN model. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator. We extend our method to video face swapping by enforcing two-temporal constraints on the latent space and the image space.
  arXiv  Detail & Related papers  (2022-03-30T00:33:08Z)
• NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction [88.02850205432763]
  We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs. Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision. We observe that the conventional volume rendering method causes inherent geometric errors for surface reconstruction. We propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision.
  arXiv  Detail & Related papers  (2021-06-20T12:59:42Z)
• Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN [16.169414324390218]
  Reconstructing seeing images from fMRI recordings is an absorbing research area in neuroscience. Visual encoding in brain is highly complex and not fully revealed. Inspired by the theory that visual features are hierarchically represented in cortex, we propose to break the complex visual signals into multi-level components.
  arXiv  Detail & Related papers  (2021-01-28T16:04:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.