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..
Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation
Authors: István Sárándi, Gerard Pons-Moll
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate our method on a variety of benchmarks: 3DPW [114] and EMDB [47] for SMPL body, AGORA [85] and EHF [87] for SMPL-X, SSP-3D [96] for SMPL focusing on body shape, as well as Human3.6M [40], MPI-INF-3DHP [70] and Mu Po TS-3D [72] for 3D skeletons. |
| Researcher Affiliation | Academia | István Sárándi,1,2 Gerard Pons-Moll1,2,3 1University of Tübingen, Germany, 2Tübingen AI Center, Germany, 3Max Planck Institute for Informatics, Saarland Informatics Campus, Germany |
| Pseudocode | Yes | In Algorithm 1, we provide the simplified pseudocode for our body model fitting algorithm used in the main paper. |
| Open Source Code | Yes | We will make our code and trained models publicly available for research. |
| Open Datasets | Yes | We extensively evaluate our method on a variety of benchmarks: 3DPW [114] and EMDB [47] for SMPL body, AGORA [85] and EHF [87] for SMPL-X, SSP-3D [96] for SMPL focusing on body shape, as well as Human3.6M [40], MPI-INF-3DHP [70] and Mu Po TS-3D [72] for 3D skeletons. |
| Dataset Splits | No | The paper mentions using test sets from various benchmarks (e.g., 3DPW, SSP-3D, AGORA) for evaluation, but it does not specify explicit training/validation splits (e.g., percentages or counts for a separate validation set) for its combined meta-dataset used during training. It describes mixed-batch training on a combination of datasets but not a dedicated validation split. |
| Hardware Specification | Yes | Training the S model takes 2 days on two 40 GB A100 GPUs, while the L takes 4 days on 8 A100s. NLF-S has a batched throughput of 410 fps and unbatched throughput of 79 fps on an Nvidia RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions several software components like Efficient Net V2-S and L [106], Adam W [66], YOLOv8 [42], Blender, and SMPLitex [14]. However, it does not provide specific version numbers for these software components, which is necessary for full reproducibility of the environment. |
| Experiment Setup | Yes | We use Efficient Net V2-S (256 px) and L (384 px) [106] initialized from [93], and train with Adam W [66], linear warmup and exponential learning rate decay for 300k steps. Training the S model takes 2 days on two 40 GB A100 GPUs, while the L takes 4 days on 8 A100s. We use random rotation, scaling, translation, truncation, color distortion, synthetic occlusion, random erasing and JPEG compression for data augmentation during training. |