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..
Diffusion4D: Fast Spatial-temporal Consistent 4D generation via Video Diffusion Models
Authors: HANWEN LIANG, Yuyang Yin, Dejia Xu, hanxue liang, Zhangyang "Atlas" Wang, Konstantinos N Plataniotis, Yao Zhao, Yunchao Wei
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our method surpasses prior state-of-the-art techniques in terms of generation efficiency and 4D geometry consistency across various prompt modalities. |
| Researcher Affiliation | Academia | Hanwen Liang1 , Yuyang Yin2 , Dejia Xu3, Hanxue Liang4, Zhangyang Wang3, Konstantinos N. Plataniotis1, Yao Zhao2, Yunchao Wei2 1University of Toronto, 2Beijing Jiaotong University, 3University of Texas at Austin, 4University of Cambridge |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We will release the code and the dynamic 3D assets idx of the dataset for reproduction of the results. |
| Open Datasets | Yes | We curate a large-scale, high-quality dynamic 3D dataset sourced from the vast 3D data corpus of Objaverse-1.0 [10] and Objaverse-XL [9]. |
| Dataset Splits | No | The paper mentions a 'test set' but does not specify a 'validation set' or its split. |
| Hardware Specification | Yes | We use a valid batch size of 128 and train on 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions specific pre-trained models like Video MV [58], Model Scope T2V [42], and I2VGen-XL [56], but does not provide specific version numbers for general software dependencies (e.g., Python, PyTorch). |
| Experiment Setup | Yes | We train our 4D-aware video diffusion model for 6k iterations with a constant learning rate of 3 10 5. We use a valid batch size of 128 and train on 8 NVIDIA A100 GPUs. During the sampling stage, we use DDIM [37] sampling with sampling step 50, and w1 = 7.0 and w2 = 0.5 in classifier-free guidance. |