Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-StyleGS: Stylized Gaussian Splatting with Multiple Styles
Authors: Yangkai Lin, Jiabao Lei, Kui Jia
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As demonstrated by our comprehensive experiments, our approach outperforms existing ones in producing plausible stylization results and offering flexible editing. Extensive experiments conducted on various datasets (Knapitsch et al. 2017; Mildenhall et al. 2019) substantiate the efficacy of our method in generating high-quality, locally matched stylized images in real-time. |
| Researcher Affiliation | Academia | 1South China University of Technology 2School of Data Science, The Chinese University of Hong Kong, Shenzhen EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology in prose, detailing the pipeline, Gaussian Splatting with Semantic Features, Preliminary of Style Loss, and Semantic Multi-style Loss, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement about releasing code, nor does it include any links to a code repository. |
| Open Datasets | Yes | We conducted extensive experiments on a diverse set of real-world scenes, including outdoor environments from the Tanks and Temples (shortened as tnt in our paper) dataset (Knapitsch et al. 2017) and forward-facing scenes from the llff dataset (Mildenhall et al. 2019). |
| Dataset Splits | No | The paper mentions using 'tnt datasets' and 'llff dataset' but does not specify exact training, validation, or testing splits, percentages, or sample counts. |
| Hardware Specification | Yes | Our novel semantic style loss can achieve memory-efficient training, which enables efficient training on a single RTX 3090. |
| Software Dependencies | No | The paper mentions models like VGG19, DINOv2, and SAM, but does not provide specific version numbers for programming languages, libraries, or other software dependencies used for implementation. |
| Experiment Setup | Yes | Our GS model is trained with Lrecon+λseg Lseg+λKNNLKNN+λNELNE+λmask Lmask, (13) where Lrecon is the Mean Squared Error (MSE) reconstruction loss as outlined in (Kerbl et al. 2023). We typically assign values of λseg = 0.02, λKNN = 0.005 , λNE = 0.005. |