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..
PhysX-3D: Physical-Grounded 3D Asset Generation
Authors: Ziang Cao, Zhaoxi Chen, Liang Pan, Ziwei Liu
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the superior performance and promising generalization capability of our framework. All the code, data, and models will be released to facilitate future research in generative physical AI. |
| Researcher Affiliation | Collaboration | Ziang Cao1 Zhaoxi Chen1 Liang Pan2 Ziwei Liu1 1Nanyang Technological University 2Shanghai AI Lab https://physx-3d.github.io/ Corresponding author, EMAIL |
| Pseudocode | No | The paper describes methods through textual descriptions and diagrams but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | All the code, data, and models will be released to facilitate future research in generative physical AI. (...) Answer: [Yes] Justification: We release the code of our generative model and examples of our dataset in the supplementary. We will release the full data as soon as possible. |
| Open Datasets | Yes | 1) To bridge the critical gap in physics-annotated 3D datasets, we present Phys XNet the first physics-grounded 3D dataset systematically annotated across five foundational dimensions: absolute scale, material, affordance, kinematics, and function description. (...) Answer: [Yes] Justification: We release the code of our generative model and examples of our dataset in the supplementary. We will release the full data as soon as possible. |
| Dataset Splits | Yes | In our experiments, we partition Phys XNet dataset into 24K training samples, 1K validation samples, and 1K test cases. |
| Hardware Specification | Yes | Our Phys XGen is trained on 8 NVIDIA A100 GPUS. |
| Software Dependencies | No | The paper mentions software components like 'GPT-4o', 'CLIP model [32]', and 'DINOv2' but does not provide specific version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | During the VAE and diffusion model training, we adopt Adam W with an initial learning rate of 1 10 4 to optimize the models. |