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EEG-Driven 3D Object Reconstruction with Style Consistency and Diffusion Prior (2410.20981v3)

Published 28 Oct 2024 in cs.CV and cs.AI

Abstract: Electroencephalography (EEG)-based visual perception reconstruction has become an important area of research. Neuroscientific studies indicate that humans can decode imagined 3D objects by perceiving or imagining various visual information, such as color, shape, and rotation. Existing EEG-based visual decoding methods typically focus only on the reconstruction of 2D visual stimulus images and face various challenges in generation quality, including inconsistencies in texture, shape, and color between the visual stimuli and the reconstructed images. This paper proposes an EEG-based 3D object reconstruction method with style consistency and diffusion priors. The method consists of an EEG-driven multi-task joint learning stage and an EEG-to-3D diffusion stage. The first stage uses a neural EEG encoder based on regional semantic learning, employing a multi-task joint learning scheme that includes a masked EEG signal recovery task and an EEG based visual classification task. The second stage introduces a latent diffusion model (LDM) fine-tuning strategy with style-conditioned constraints and a neural radiance field (NeRF) optimization strategy. This strategy explicitly embeds semantic- and location-aware latent EEG codes and combines them with visual stimulus maps to fine-tune the LDM. The fine-tuned LDM serves as a diffusion prior, which, combined with the style loss of visual stimuli, is used to optimize NeRF for generating 3D objects. Finally, through experimental validation, we demonstrate that this method can effectively use EEG data to reconstruct 3D objects with style consistency.

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Summary

  • The paper introduces a two-stage framework that converts EEG signals into latent codes for precise 3D object reconstruction.
  • It employs a diffusion model integrated with neural style transfer and NeRF to ensure both color consistency and geometric fidelity.
  • Quantitative evaluations using FID, IS, SSIM, and LPIPS demonstrate significant improvements over existing EEG-based visual reconstruction methods.

EEG-Driven 3D Object Reconstruction with Color Consistency and Diffusion Prior

The paper explores a novel approach for reconstructing three-dimensional (3D) objects with color consistency by utilizing electroencephalogram (EEG) signals. The work is positioned at the intersection of neuroscience and artificial intelligence, addressing key challenges in accurately capturing visual perceptual information through EEG data. This paper proposes a two-stage framework that integrates implicit neural encoding and decoding processes, leveraging both neural style transfer and Neural Radiance Fields (NeRF) within a diffusion model framework to achieve its objectives.

Overview

The method involves reconstructing 3D objects by first training an implicit neural EEG encoder capable of perceiving 3D objects and capturing regional semantic features. Subsequently, the paper employs a latent diffusion model to decode these features into 3D objects. The focus on color consistency is particularly significant because it represents an advancement in retaining the true visual characteristics perceived by the human brain.

In the first stage, the model processes EEG signals to produce latent codes, which capture the spatial and semantic information necessary for 3D reconstruction. These latent codes are integrated into a diffusion model, along with neural style loss and NeRF, to ensure the produced 3D objects retain color attributes consistent with their real-world counterparts.

Techniques and Contributions

  1. EEG Encoder Training: The paper proposes a dual-task approach for EEG encoder training, which combines the tasks of reconstruction and semantic classification. This mechanism enables the encoder to learn temporal, spatial, and semantic characteristics of EEG, essential for high-fidelity 3D reconstructions.
  2. Diffusion Model Integration: The diffusion model, fine-tuned using latent EEG codes, comes into play to transform the latent space semantic information into detailed 2D and 3D representations.
  3. Color Consistency through Style Transfer: A neural style transfer mechanism ensures the color attributes of the reconstructed objects remain consistent with ground truth images, thus enhancing the perceptual realism.
  4. NeRF Utilization: By incorporating NeRF, the framework is capable of rendering consistent 3D geometry and appearance across various viewpoints. This ensures not only color accuracy but also geometric integrity of the reconstructed objects.

Results and Evaluation

The paper reports several promising outcomes through qualitative and quantitative evaluation metrics, such as FID, IS, and SSIM for 2D images, as well as LPIPS and Contextual metrics for 3D objects. The proposed method showcases significant improvements over existing EEG-based image generation models, demonstrating superior performance in generating perceptually accurate 3D objects with consistent color.

Implications and Future Directions

The findings of this research have several implications:

  • Practical Applications: The ability to reconstruct accurate 3D visuals from EEG signals holds potential for numerous applications, especially in the fields of brain-computer interfaces, virtual reality, and neuroimaging.
  • Theoretical Insights: The paper advances theoretical understanding of how visual perception encoded in EEG can be translated into complex visual reconstructions, suggesting deeper insights into human visual processing.
  • Future Research: This methodology opens avenues for further exploration into how EEG signals can encode other aspects of visual perception and how these signals can be harnessed to control and generate complex visual representations.

Overall, this research contributes to the growing intersection of neuroscience, computer vision, and AI, showcasing how sophisticated modeling techniques can reconstruct intricate visual experiences from neural data. The dual focus on semantic accuracy and color consistency is a notable advancement that addresses longstanding challenges in EEG-based visual reconstruction.

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