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..
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
Authors: Mingzhen Sun, Weining Wang, Zihan Qin, Jiahui Sun, Sihan Chen, Jing Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method1, and new state-of-the-art results have been achieved on multiple benchmarks. |
| Researcher Affiliation | Academia | 1Institute of Automation, Chinese Academy of Sciences (CASIA) 2School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) |
| Pseudocode | No | The paper describes its methods in prose and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes have been released in https://github.com/iva-mzsun/GLOBER |
| Open Datasets | Yes | Table 1 and Table 2 reports the results of our model trained on the Sky Time-lapse [36], Tai Chi-HD [37], UCF-101 [38], and Webvid-10M [39] datasets for 16-frame video generation in both unconditional and conditional settings. |
| Dataset Splits | No | The paper mentions training on specific datasets (UCF101, Tai Chi HD, and Sky Time-lapse) but does not provide specific details on how these datasets were split into training, validation, or test sets for their experiments. |
| Hardware Specification | Yes | All experiments are implemented using Py Torch [40] and conducted on 8 NVIDIA A100 GPUs, with 16-precision adopted for fast training." and "Results with * are taken from PVDM and measured with a single NVIDIA 3090ti 24GB GPU. The rest are evaluated on a single NVIDIA 3090 24GB GPU by us due to lack of 3090ti. |
| Software Dependencies | No | The paper states 'All experiments are implemented using Py Torch [40]' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The video auto-encoder was trained with a batch size of 40 per GPU for 80K, 40K, and 40K steps on the UCF101, Tai Chi HD, and Sky Time-lapse datasets, respectively. The loss weight λ1 and λ2 are set as 1e-6 and 0.1, respectively. |