Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Dense Hand Contact Estimation from Imbalanced Data
Authors: Daniel Jung, Kyoung Mu Lee
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 28,827 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,907 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments 5.1 Datasets 5.2 Evaluation metrics 5.3 Ablation study 5.4 Comparison with state-of-the-art methods |
| Researcher Affiliation | Academia | 1IPAI, 2Dept. of ECE & ASRI, Seoul National University, Korea EMAIL |
| Pseudocode | No | The paper describes the methodology in prose and figures, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/dqj5182/HACO_RELEASE. |
| Open Datasets | Yes | We select 14 datasets with diverse hand interactions including Ob Man [19], Dex YCB [5], MOW [4], HO3D [15], H2O3D [16], ARCTIC [12], HOI4D [36], H2O [24] for hand-object interaction, Inter Hand2.6M [41], HIC [59] for hand-hand interaction, PROX [17], RICH [22] for hand-scene interaction, and Decaf [55], Hi4D [65] for hand-body interaction. |
| Dataset Splits | Yes | To reduce redundancy from large video datasets, we employ sampling ratio of 5, 10, 5 for HOI4D [36], Inter Hand2.6M [41], and Decaf [55] dataset, respectively. ... For the RICH dataset, we follow the official split used in BSTRO [22] for fair comparison. |
| Hardware Specification | Yes | We train HACO for 10 epochs on a single NVIDIA A6000 GPU. |
| Software Dependencies | No | Py Torch [47] is used for implementation. Our backbone is initialized with the pre-trained weights of publicly released Ha Me R [49]. |
| Experiment Setup | Yes | We use the Adam W optimizer [38] with a learning rate of 10 5 and a mini-batch size of 24. For stable convergence, the learning rate is reduced by a factor of 0.9 after 5 and 10 epochs. We train HACO for 10 epochs on a single NVIDIA A6000 GPU. |