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..
Motion Decoupled 3D Gaussian Splatting for Dynamic Object Representation
Authors: Xiao Hu, Libo Long, Jochen Lang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Evaluation We select five state-of-the-art 3D representation methods as comparators. ... The evaluation metrics follow the previous public benchmarks (Pumarola et al. 2021; Li et al. 2021). Specifically, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and VGG-based Learned Perceptual Image Patch Similarity (LPIPS) (Zhang et al. 2018) are used. |
| Researcher Affiliation | Academia | Xiao Hu, Libo Long, Jochen Lang University of Ottawa, Canada, EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are present in the paper. The methodology is described in prose and mathematical formulas. |
| Open Source Code | Yes | Code https://github.com/haliphinx/M5D-GS |
| Open Datasets | Yes | Both the dataset with a total of ten scenes and the source files used for its creation are available open-source, allowing the community to further investigate severe motion understanding. Current public datasets (Pumarola et al. 2021; Li et al. 2021; Yan, Li, and Lee 2023) for dynamic scene representation usually contain only slight motion and deformation. |
| Dataset Splits | No | The paper introduces a novel dataset and augments existing ones, but it does not specify explicit training/validation/test splits (e.g., percentages, sample counts, or specific files) for its experiments within the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The main constraints for the proposed M5D-GS still follow the original 3D-GS without additional loss for motion estimation. The overall constraints include a per-pixel L1 loss and a D-SSIM loss (Kerbl et al. 2023) LD SSIM. The loss function is Limg = L1 + λLD SSIM with λ as the loss coefficient. More details are available in the supplementary material. |