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 Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses
Authors: Seungwoo Yoo, Juil Koo, Kyeongmin Yeo, Minhyuk Sung
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with the Deform Things4D and Human datasets demonstrate state-of-the-art performance in pose transfer and the ability to generate diverse deformed shapes with various objects and poses. |
| Researcher Affiliation | Academia | Seungwoo Yoo Juil Koo Kyeongmin Yeo Minhyuk Sung KAIST EMAIL |
| Pseudocode | No | The paper does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | We will publicly release the code upon acceptance. |
| Open Datasets | Yes | For the animal shapes, we utilize animation sequences from the Deforming Things4D-Animals dataset [23]. For humanoids, we use SMPL [28, 31]... Additionally, we collect 9 stylized character meshes from the Mixamo dataset [1]. |
| Dataset Splits | No | The paper describes data used for training and testing, but it does not explicitly define a validation set or specific train/validation/test split percentages across the datasets used for general model training. |
| Hardware Specification | Yes | Our experiments are conducted on RTX 3090 GPUs (24 GB VRAM) and A6000 GPUs (48 GB VRAM). |
| Software Dependencies | No | The paper mentions using 'Point Transformer layers from Zhao et al.[56] and Tang et al.[44]' and that 'network architectures for our cascaded diffusion models... are adapted from Koo et al. [21]', but it does not provide specific version numbers for these or other software libraries or dependencies. |
| Experiment Setup | Yes | These models operate over T = 1000 timesteps with a linear noise schedule ranging from β1 = 1 10 4 to βT = 5 10 2. For model training, we employ the ADAM optimizer at a learning rate of 1 10 3 and standard parameters. For per-identity refinement modules, we set λlap = 1.0, λedge = 1.0, and λreg = 5 10 2 during training. |