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..
Towards Smooth Video Composition
Authors: Qihang Zhang, Ceyuan Yang, Yujun Shen, Yinghao Xu, Bolei Zhou
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on a range of datasets and show substantial improvements over baselines on video generation. |
| Researcher Affiliation | Collaboration | Qihang Zhang1 Ceyuan Yang2 Yujun Shen3 Yinghao Xu1 Bolei Zhou4 1The Chinese University of Hong Kong, 2Shanghai AI Laboratory, 3Ant Group, 4University of California, Los Angeles |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and models are publicly available at https://genforce.github. io/Style SV. |
| Open Datasets | Yes | We evaluate our approach on a range of datasets and show substantial improvements over baselines on video generation. Code and models are publicly available at https://genforce.github. io/Style SV. |
| Dataset Splits | No | The paper mentions evaluating results with the highest FVD16 score after training, which implies a validation step, but it does not explicitly provide specific dataset split information (percentages, counts, or predefined splits) for training, validation, and testing needed to reproduce the data partitioning. |
| Hardware Specification | Yes | We follow the training receipt of Style GAN-V and train models on a server with 8 A100 GPUs. |
| Software Dependencies | No | The paper states, 'Our method is developed based on the official implementation of Style GAN-V (Skorokhodov et al., 2022),' but it does not provide specific version numbers for software components like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | In terms of various methods and datasets, we grid search the R1 regularization weight, whose details are available in Appendix. Empirically, we find that a smaller R1 value (e.g., 0.25) works well for pretraining stage (Config-C). While a larger R1 value (e.g., 4) better suits to video generation learning. |