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..
4Diffusion: Multi-view Video Diffusion Model for 4D Generation
Authors: Haiyu Zhang, Xinyuan Chen, Yaohui WANG, Xihui Liu, Yunhong Wang, Yu Qiao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive qualitative and quantitative experiments demonstrate that our method achieves superior performance compared to previous methods. |
| Researcher Affiliation | Academia | Haiyu Zhang1,2 , Xinyuan Chen2, Yaohui Wang2, Xihui Liu3, Yunhong Wang1, Yu Qiao2 1Beihang University 2Shanghai AI Laboratory 3The University of Hong Kong 1EMAIL 2EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release our code and data at https://aejion.github.io/4diffusion. |
| Open Datasets | Yes | We utilize Objaverse dataset [11] to train 4DM. |
| Dataset Splits | No | The paper mentions using a 'curated subset' for training and 'test cases' for evaluation, but does not specify a validation set split or its details. |
| Hardware Specification | Yes | The training takes about 2 days with 16 NVIDIA Tesla A100 GPUs. |
| Software Dependencies | No | The paper mentions 'Stable Diffusion framework' and 'threestudio framework' but does not provide specific version numbers for these or other software dependencies like Python or PyTorch. |
| Experiment Setup | Yes | We train 4DM with multi-view videos with 256 256 resolutions for 30,000 steps with a batch size of 32, using the Adam W optimizer with a learning rate of 1e-4. |