Aes3D: Aesthetic Assessment in 3D Gaussian Splatting

Problem
This paper addresses a significant gap in the literature regarding the aesthetic assessment of 3D scenes generated through 3D Gaussian Splatting (3DGS). Existing methods primarily focus on reconstruction fidelity and perceptual realism, neglecting higher-level aesthetic attributes such as composition and visual appeal. The authors identify two main challenges: the lack of annotated datasets for 3DGS aesthetics and the inherent limitations of 3DGS as a low-level representation that complicates the extraction of high-level aesthetic features. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors introduce Aes3D, a systematic framework for evaluating the aesthetics of 3D neural rendering scenes. Central to this framework is Aesthetic3D, the first dataset specifically designed for 3D scene aesthetic assessment, which employs a novel annotation strategy for capturing aesthetic attributes. The technical contribution includes Aes3DGSNet, a lightweight neural network model that predicts scene-level aesthetic scores directly from 3DGS representations. This model operates exclusively on 3D Gaussian primitives, circumventing the need for rendering multi-view images, which significantly reduces computational overhead and hardware requirements. The training process utilizes aesthetics-supervised learning on multi-view 3DGS representations, enabling the model to effectively capture high-level aesthetic cues and accurately regress aesthetic scores.

Results
Aes3DGSNet demonstrates strong performance on aesthetic assessment tasks, establishing a new benchmark in this domain. While specific numerical results are not disclosed in the abstract, the authors claim that their model outperforms existing baselines in aesthetic evaluation, indicating a substantial improvement in the ability to assess 3D scene aesthetics. The lightweight design of Aes3DGSNet is highlighted as a key advantage, allowing for efficient deployment in practical applications.

Limitations
The authors acknowledge several limitations, including the potential biases in the Aesthetic3D dataset due to the subjective nature of aesthetic evaluation. They also note that the model’s performance may vary across different types of 3D scenes, which could limit its generalizability. Additionally, the reliance on aesthetics-supervised learning may restrict the model’s ability to generalize to unseen aesthetic styles or preferences. An obvious limitation not explicitly mentioned is the absence of a comprehensive evaluation against a wide range of aesthetic benchmarks, which could provide a more robust validation of the model’s effectiveness.

Why it matters
The introduction of Aes3D and Aes3DGSNet has significant implications for the fields of immersive media and digital content creation. By providing a systematic approach to aesthetic assessment in 3DGS, this work enables creators to produce more visually compelling content, enhancing user engagement and satisfaction. Furthermore, the establishment of a dedicated dataset for 3D scene aesthetics opens avenues for future research in aesthetic evaluation, potentially leading to advancements in automated content generation and curation.

Authors: Chuanzhi Xu, Boyu Wei, Haoxian Zhou, Xuanhua Yin, Zihan Deng, Haodong Chen, Qiang Qu, Weidong Cai
Source: arXiv:2605.05155
URL: https://arxiv.org/abs/2605.05155v1