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 [1].
Implicit Warping for Animation with Image Sets
Authors: Arun Mallya, Ting-Chun Wang, Ming-Yu Liu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform our ablations on the Talking Head-1KH [41] dataset at the 256ห256 resolution and compare with baselines at the full 512 ห 512 resolution. We also report results on the 256 ห 256 Vox Celeb2 [26] dataset. Additionally, to demonstrate the generality of our method, we report results on the more challenging TED Talk [28] dataset of moving upper bodies at a resolution of 384 ห 384. Metrics. We measure the ๏ฌdelity of driving image reconstruction using PSNR, L1, LPIPS [53], and FID [12]. |
| Researcher Affiliation | Industry | Arun Mallya EMAIL Ting-Chun Wang EMAIL Ming-Yu Liu EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | We will release the code after acceptance. |
| Open Datasets | Yes | Datasets. We perform our ablations on the Talking Head-1KH [41] dataset at the 256ห256 resolution and compare with baselines at the full 512 ห 512 resolution. We also report results on the 256 ห 256 Vox Celeb2 [26] dataset. Additionally, to demonstrate the generality of our method, we report results on the more challenging TED Talk [28] dataset of moving upper bodies at a resolution of 384 ห 384. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See provided supplementary material. |
| Hardware Specification | No | The paper mentions performance in terms of FPS ('reasonably fast ( 10 FPS on 512 ห 512 images)') but does not specify the hardware (e.g., GPU model, CPU, memory) used for experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See provided supplementary material. |