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..
Generalizable Implicit Motion Modeling for Video Frame Interpolation
Authors: Zujin Guo, Wei Li, Chen Change Loy
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present quantitative and qualitative evaluations of our motion modeling method GIMM in Section 4.1, and the corresponding interpolation method (GIMM-VFI) in Section 4.4. Specifically, we evaluate both motion quality and performance on the downstream interpolation task. We compare GIMM-VFI with current state-of-the-art VFI methods on arbitrary-timestep interpolation. |
| Researcher Affiliation | Academia | Zujin Guo, Wei Li, Chen Change Loy S-Lab, Nanyang Technological University EMAIL |
| Pseudocode | No | The paper includes architectural diagrams (Figure 6, 7, 8) but does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | We are unable to provide our code upon submission, but releasing the code to the public in the future is our plan. |
| Open Datasets | Yes | We train the GIMM model on the training split of Vimeo90K [54] triplets dataset using optical flows extracted by off-the-shelf flow estimators. |
| Dataset Splits | No | We train the GIMM model on the training split of Vimeo90K [54] triplets dataset... Our GIMM-VFI is trained on the complete Vimeo90K septuplet dataset. Specifically, we implement two variants of GIMM-VFI, using two different flow estimators: the RAFT [50] and Flow Former [19], designated as GIMM-VFI-R and GIMM-VFI-F, respectively. However, both versions of GIMM-VFI share the same training process. Similar to previous works [55, 20], we train our model on the complete Vimeo90K septuplet split [54] for 60 epochs with a batch size of 32 and a learning rate of 8 10 5. We randomly select triple subsets for training from each septuplet, following the same sampling strategy as previous research [55, 20]. |
| Hardware Specification | Yes | using 2 NVIDIA V100 GPUs. [...] We train our model on 8 NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions software components like 'Adam W' optimizer, 'Cosine annealing' learning rate schedule, and 'PReLU function', and references 'RAFT [50]' and 'Flow Former [19]' as flow estimators, but does not provide specific version numbers for any of these. |
| Experiment Setup | Yes | We train the GIMM model on the training split of Vimeo90K [54] triplets dataset using optical flows extracted by off-the-shelf flow estimators. [...] randomly cropping the flows to a resolution of 256 256. For each batch during training, we randomly select a timestep t from the set {0, 0.5, 1} to supervise. We set the batch size to 64, and train the model for 240 epochs with a learning rate of 1 10 4. [...] We resize and randomly crop each frame into a resolution of 224 224 and perform a series of augmentations including rotation, flipping, temporal order reversing and channel order reversing. [...] for 60 epochs with a batch size of 32 and a learning rate of 8 10 5. Table 5 also provides: Optimizer Adam W, Peak learning rate, Minimum learning rate, Epochs, Batch size per GPU, Weight decay, Optimizer momentum β1, β2 = 0.9, 0.999, Learning rate schedule 55 Cosine annealing, Warmip epochs, Training Resolution. |