PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation
Authors: Zhaoxi Chen, Fangzhou Hong, Haiyi Mei, Guangcong Wang, Lei Yang, Ziwei Liu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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'. |