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..
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
Authors: Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, Kai Zhang
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS4.1 EXPERIMENTAL SETTINGS4.2 EXPERIMENT RESULTS4.3 ABLATION STUDIES |
| Researcher Affiliation | Collaboration | Peng Wang Adobe Research & HKU EMAIL Hao Tan Adobe Research EMAIL Sai Bi Adobe Research EMAIL Yinghao Xu Adobe Research & Stanford EMAIL Fujun Luan Adobe Research EMAIL Kalyan Sunkavalli Adobe Research EMAIL Wenping Wang Texas A&M University EMAIL Zexiang Xu Adobe Research EMAIL Kai Zhang Adobe Research EMAIL |
| Pseudocode | No | The paper describes the pipeline with diagrams and mathematical equations, but does not include an explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our project website is at: https://totoro97.github.io/pf-lrm. |
| Open Datasets | Yes | We use a mixture of multi-view posed renderings from Objaverse (Deitke et al., 2023) and posed real captures from MVImg Net (Yu et al., 2023) for training. |
| Dataset Splits | No | The paper states 'Our model only requires multi-view posed images to train' and details evaluation on various datasets, but does not explicitly provide information on train/validation/test splits for its own training data beyond using separate evaluation datasets. |
| Hardware Specification | Yes | 1.3 seconds on a single A100 GPU. ... It has 24 self-attention layers with 1024 token dimension, and is trained on 8 A100 GPUs for 20 epochs (~100k iterations), which takes around 5 days. In addition, to show the scaling law with respect to model sizes, we train a large model (Ours (L)) on 128 GPUs for 100 epochs (~70k iterations). |
| Software Dependencies | No | The paper mentions using Adam W optimizer and Flash Attention V2, but does not provide specific version numbers for these or any other software dependencies like PyTorch or Python. |
| Experiment Setup | Yes | We set the loss weights γ C, γ C , γp, γα, γy to 1, 2, 1, 1, 1, respectively. For more details, please refer to Sec. A.3 of the appendix. ... We use Adam W (Loshchilov & Hutter, 2017) (β1 = 0.9, β2 = 0.95) optimizer with weight decay 0.05 for model optimization. The initial learning rate is zero, which is linearly warmed up to 4e-4 for the first 3k steps and then decay to zero by cosine scheduling. |