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..
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation
Authors: Zhaoxi Chen, Fangzhou Hong, Haiyi Mei, Guangcong Wang, Lei Yang, Ziwei Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate that Prim Diffusion outperforms state-of-the-art methods in 3D human generation. |
| Researcher Affiliation | Collaboration | Zhaoxi Chen1 Fangzhou Hong1 Haiyi Mei2 Guangcong Wang1 Lei Yang2 Ziwei Liu1,B 1S-Lab, Nanyang Technological University 2Sensetime Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions supplementary material for more details and video results, but does not explicitly state that the source code for their method is released or provide a direct link to it. |
| Open Datasets | Yes | We obtain 796 high-quality 3D humans from Render People [55] with diverse identities and clothes. ... [55] https://renderpeople.com/3d-people/. Renderpeople, 2018. ...finetuned EVA3D that is first pre-trained on Deep Fashion [26] dataset |
| Dataset Splits | No | The paper states: 'All methods are trained from scratch (except mentioned) on this dataset for fair comparisons.' and 'The metrics PSNR, SSIM [58], and LPIPS [63] are averaged across all training identities, views, and poses.' It does not explicitly mention training/validation/test splits for the dataset used in the main experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as Python, PyTorch, or CUDA versions. |
| Experiment Setup | No | The paper briefly mentions the number of primitives (K=1024) and the denoiser architecture (2D U-Net) but does not provide specific experimental setup details such as hyperparameters, learning rates, batch sizes, or optimizer settings in the main text, deferring to supplementary material for 'more details'. |