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..
Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion
Authors: Jingyuan Chen, Fuchen Long, Jie An, Zhaofan Qiu, Ting Yao, Jiebo Luo, Tao Mei
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments of long video generation on the VBench benchmark demonstrate the superiority of our Ouroboros-Diffusion, particularly in terms of subject consistency, motion smoothness, and temporal consistency. ... Extensive experiments on VBench verify the effectiveness of our proposal in terms of both visual and motion quality. |
| Researcher Affiliation | Collaboration | 1 University of Rochester, Rochester, NY USA 2 Hi Dream.ai Inc. |
| Pseudocode | No | The paper describes methods like 'Coherent tail latent sampling', 'Subject-Aware Cross-Frame Attention (SACFA)', and 'Self-Recurrent Guidance' using mathematical formulations and descriptive text, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing code or a link to a code repository. |
| Open Datasets | Yes | We empirically verify the merit of our Ouroboros-Diffusion for both single-scene and multi-scene long video generation on the VBench (Huang et al. 2024) benchmark. |
| Dataset Splits | Yes | We sample 93 common prompts from VBench as the testing set for single-scene video generation. ... For each multi-prompt group, we generate 256 video frames for performance comparison. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper states: 'We implement our Ouroboros Diffusion on the text-to-video model Video Crafter2 (Chen et al. 2024a).' However, it does not provide specific version numbers for underlying software libraries (e.g., PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | The total number of time steps T in the DDIM sampler is set to 64, matching the queue length. The threshold for the low-pass filter in coherent tail latent sampling is set to 0.25. SACFA is applied only in the down-blocks and mid-block (with down-sampling factors of 2 and 4) of the spatial-temporal UNet empirically. The last 16 frames in the queue are involved in SACFA calculation. The self-recurrent guidance derived from the first 16 frames at the queue head applies to the last 16 frames at the tail. The parameter λ for updating the subject feature bank is set to 0.98. |