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..
CigTime: Corrective Instruction Generation Through Inverse Motion Editing
Authors: Qihang Fang, Chengcheng Tang, Bugra Tekin, Yanchao Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present both qualitative and quantitative results across a diverse range of applications that largely improve upon baselines. Our approach demonstrates its effectiveness in instructional scenarios, offering text-based guidance to correct and enhance user performance. |
| Researcher Affiliation | Collaboration | Qihang Fang1, Chengcheng Tang2, Bugra Tekin2, and Yanchao Yang1* 1The University of Hong Kong 2Meta Reality Labs {qihfang}@gmail.com, EMAIL, {yanchaoy}@hku.hk |
| Pseudocode | No | The paper describes the methodology using text and equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We will release the codes and our generated dataset after acceptance. |
| Open Datasets | Yes | Datasets We obtain the source motion sequences from Human ML3D [13], a dataset containing 3D human motions and associated language descriptions. ... To evaluate the generalization ability of our algorithm, we collected 1525 samples from the Fit3D [11] dataset. ... We further evaluate our method baselines on KIT dataset. |
| Dataset Splits | Yes | We split Human ML3D following the original setting and for each motion sequence in Human ML3D, we randomly select one instruction from the corresponding split for editing the sequence. |
| Hardware Specification | Yes | We use a batch size of 512 and train on four NVIDIA Tesla A100 GPUs for eight epochs, which takes approximately 5 hours to complete. |
| Software Dependencies | No | The paper mentions models like 'Llama-3-8B' and 'Adam optimizer' but does not provide specific version numbers for programming languages, libraries (e.g., PyTorch), or other ancillary software dependencies. |
| Experiment Setup | Yes | We fine-tune a pre-trained Llama-3-8B [30] using full-parameter fine-tuning for corrective instruction generation. The model is optimized using the Adam optimizer with an initial learning rate of 10 5. We use a batch size of 512 and train on four NVIDIA Tesla A100 GPUs for eight epochs, which takes approximately 5 hours to complete. |