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..
NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics
Authors: Junyi Cao, Shanyan Guan, Yanhao Ge, Wei Li, Xiaokang Yang, Chao Ma
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on various dynamics in terms of grounded particle accuracy, dynamic rendering quality, and generalization ability demonstrate that Neu MA can accurately capture intrinsic dynamics. |
| Researcher Affiliation | Collaboration | 1 Mo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University 2 vivo Mobile Communication Co., Ltd. EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Particle Binding |
| Open Source Code | Yes | Project Page: https://xjay18.github.io/projects/neuma.html. |
| Open Datasets | Yes | For a comprehensive evaluation, we consider both synthetic and real-world data. Synthetic Data. Following PAC-Ne RF [45] and NCLaw [53], we use MPM [34] to simulate 6 kinds of dynamics... The generated 6 benchmarks are named Bouncy Ball , Jelly Duck , Rubber Pawn 1, Clay Cat , Honey Bottle , and Sand Fish . We report the simulation details in Appendix B. Real-world Data. We adopt the real-world data provided by Spring-Gaus [96] to assess the visual grounding performance in the real world. |
| Dataset Splits | Yes | To ensure an accurate object appearance and geometry, we additionally render 50 uniformly sampled viewpoints at t = 0, with the cameras evenly spaced on a sphere covering the object of interest, and use these data for static reconstruction. |
| Hardware Specification | Yes | All the experiments are conducted on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions software like Blender [9], RAdam optimizer [49], and MPM [34], but it does not provide specific version numbers for these software components or libraries. |
| Experiment Setup | Yes | We optimize the neural material adaptor MĪø using RAdam [49] optimizer with a cosine learning rate scheduler for 1, 000 iterations for each scene. We choose Ļ = 95% and Ļ = 1, 000 for all experiments unless otherwise specified. Our MPM simulator operates in a [0, 1]3 cube with a fixed resolution of 323 and 703 for synthetic and real-world data unless otherwise specified. |