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..
Splatter a Video: Video Gaussian Representation for Versatile Processing
Authors: Yang-Tian Sun, Yihua Huang, Lin Ma, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan Qi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments, Evaluation We conducted experiments on the DAVIS dataset [36] as well as some videos used by Omnimotion [48] and Co De F [33]. Our approach is evaluated based on two criteria: 1) reconstructed video quality and 2) downstream video processing tasks. In addition to general video representation methods Deformable Sprites [58], Omnimotion [48] and Co De F [33], we also compare with dynamic Ne RF/3DGS methods, namely 4DGS [51] and Ro Dyn RF [23]. |
| Researcher Affiliation | Collaboration | Yang-Tian Sun1 Yi-Hua Huang1 Lin Ma Xiaoyang Lyu1 Yan-Pei Cao2 Xiaojuan Qi1 1The University of Hong Kong 2 VAST |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | Code and data are not provided for now but will be released to the public. |
| Open Datasets | Yes | Evaluation We conducted experiments on the DAVIS dataset [36] as well as some videos used by Omnimotion [48] and Co De F [33]. |
| Dataset Splits | No | The paper mentions using the DAVIS dataset but does not provide specific training, validation, or test splits such as percentages or sample counts. |
| Hardware Specification | Yes | The training duration is approximately 15-20 minutes on an NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions using RAFT [44], Marigold [15], SAM [17], and DINOv2 [31] but does not provide specific version numbers for these or other software libraries/frameworks used for implementation. |
| Experiment Setup | Yes | Typically, we use a video clip of about 50-100 frames and train the system iteratively for 20,000 steps. ... The Gaussians are initialized as 10,0000 points randomly sampled in a [ 1, 1] [ 1, 1] [0, 1] box. ... Every 100 steps, Gaussians with an accumulated gradient scale of positions above a threshold will be densified. ... The loss weights for render, depth, flow, motion regularization, and label are set to λrender = 5.0, λdepth = 1.0, λflow = 2.0, λarap = 0.1, and λlabel = 1.0. (Table 3 also provides specific learning rates for various attributes). |