ConditionVideo: Training-Free Condition-Guided Video Generation
Authors: Bo Peng, Xinyuan Chen, Yaohui Wang, Chaochao Lu, Yu Qiao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, 5.1 Implementation Details, 5.2 Main results, 5.3 Comparison, 5.4 Ablation Study |
| Researcher Affiliation | Collaboration | Bo Peng1,2*, Xinyuan Chen2 , Yaohui Wang2, Chaochao Lu2, Yu Qiao2 1Shanghai Jiao Tong University 2Shanghai Artificial Intelligence Laboratory {pengbo,chenxinyuan,wangyaohui,luchaochao,qiaoyu}@pjlab.org.cn |
| Pseudocode | Yes | Algorithm 1: Sampling Algorithm |
| Open Source Code | No | For the project website, see https://pengbo807.github.io/conditionvideo-website/ |
| Open Datasets | No | The paper states "The conditions are randomly generated from a group of 120 different videos" and mentions fine-tuning on videos, but provides no concrete access information (link, DOI, repository, or formal citation) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions "Stable Diffusion 1.5" but does not provide specific software dependencies (e.g., library or solver names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | We generate 24 frames with a resolution of 512 × 512 pixels for each video. During inference, we use the same sampling setting as Tune-A-Video (Wu et al. 2022b). |