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..
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
Authors: Zijie Pan, Jiachen Lu, Xiatian Zhu, Li Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that PGC can serve as a generic integrative plug-in, consistently benefiting existing SDS and LDM-based 3D generative models, leading to significant improvements in high-resolution 3D texture synthesis. 5 EXPERIMENTS |
| Researcher Affiliation | Academia | Zijie Pan1, Jiachen Lu1, Xiatian Zhu2, Li Zhang1 1School of Data Science, Fudan University 2University of Surrey |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | https://fudan-zvg.github.io/PGC-3D/ |
| Open Datasets | Yes | For evaluation, we use ridge regression methods with the COCO dataset (Lin et al., 2014) to determine the optimal configuration. |
| Dataset Splits | No | The paper mentions using a "uniform setting" but does not specify exact percentages or sample counts for training, validation, or test splits. It does not reference predefined splits with citations for dataset partitioning. |
| Hardware Specification | Yes | Specifically, we optimize the same texture and/or signed distance function (SDF) fields as Chen et al. (2023b) for 1200 iterations on two A6000 GPUs with batch size 4 by using Adam optimizer without weight decay. |
| Software Dependencies | No | The paper mentions software components like "Adam optimizer", "Stable Diffusion", "SDXL", "VAE", and "U-net" but does not provide specific version numbers for these, or for general programming environments like Python or PyTorch. |
| Experiment Setup | Yes | For all the experiments, we adopt the uniform setting without any hyperparameter tuning. Specifically, we optimize the same texture and/or signed distance function (SDF) fields as Chen et al. (2023b) for 1200 iterations on two A6000 GPUs with batch size 4 by using Adam optimizer without weight decay. The learning rates are set to constant 1 10 3 for texture field and 1 10 5 for SDF field. For the sampling, we set the initial mesh normalized in [ 0.8, 0.8]3, focal range [0.7, 1.35], radius range [2.0, 2.5], elevation range [ 10 , 45 ] and azimuth angle range [0 , 360 ]. In SDS, we set CFG 100, t U(0.02, 0.5) and w(t) = σ2 t . In PGC, we use PGC-N for PGC as default and set the threshold c = 0.1. The clipping threshold is studied in Section A.3. |