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..
FocalDreamer: Text-Driven 3D Editing via Focal-Fusion Assembly
Authors: Yuhan Li, Yishun Dou, Yue Shi, Yu Lei, Xuanhong Chen, Yi Zhang, Peng Zhou, Bingbing Ni
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have highlighted the superior editing capabilities of Focal Dreamer in both quantitative and qualitative evaluations. |
| Researcher Affiliation | Collaboration | Yuhan Li1, Yishun Dou2, Yue Shi 1, Yu Lei 1, Xuanhong Chen 1, Yi Zhang 1, Peng Zhou 1, Bingbing Ni1 1Shanghai Jiao Tong University, Shanghai 200240, China 2Huawei |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the method in prose and through diagrams. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of its source code. |
| Open Datasets | No | We assemble the dataset with 15 high-quality meshes found on the internet. The paper describes assembling its own dataset but does not provide concrete access information (link, DOI, citation) for public availability. |
| Dataset Splits | No | The paper mentions using a 'Synthetic Object Dataset' of '15 high-quality meshes' and conducting user studies with '65 participants' but does not specify any training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | Yes | Focal Dreamer usually takes less than 30 minutes (3000 steps) for geometry and 20 minutes (2000 steps) for texture to converge on 4 Nvidia RTX 3090 GPUs |
| Software Dependencies | No | We use the Stable Diffusion implementation by Hugging Face Diffusers for SDS, and adopt DMTet to learn geometry and texture separately with NVDiff Rast as a differentiable renderer. The paper lists software components but does not provide specific version numbers for them. |
| Experiment Setup | Yes | We use Adam W optimizer with the respective learning rates of 1 10 3 and 1 10 2. Focal Dreamer usually takes less than 30 minutes (3000 steps) for geometry and 20 minutes (2000 steps) for texture to converge. The hyperparameter ΞΎ is a small positive threshold to prevent topological structures from minor positive SDF values. Moreover, the closer pi is to the target region, the less the penalty, for this distance-aware setting permits geometry to overrun beyond the rough focal region slightly. Ο1 = 0.05 and Ο2 = 0.01 control how sensitive the loss is. k determines the extent of the soft merge and is set to 0.15 by default. |