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 [1].
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. |