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].
TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction
Authors: Yunfei Liu, Lei Zhu, Lijian Lin, Ye Zhu, Ailing Zhang, Yu Li
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative experimental results across multiple datasets demonstrate that TEASER achieves state-of-the-art performance in precise expression reconstruction. |
| Researcher Affiliation | Academia | 1International Digital Economy Academy 2Peking University, Shenzhen Graduate School EMAIL. The email domain '.edu.cn' along with the explicit mention of 'Peking University, Shenzhen Graduate School' (an academic institution) indicates an academic affiliation. |
| Pseudocode | No | The paper describes methods textually and with mathematical equations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and demos are available at https://tinyurl.com/TEASER-project. |
| Open Datasets | Yes | We use the following datasets for training: FFHQ Karras et al. (2019), Celeb A Liu et al. (2015a), and LRS3 Afouras et al. (2018). |
| Dataset Splits | Yes | We follow Retsinas et al. (2024) and separate different videos for training and testing. |
| Hardware Specification | Yes | All models are trained on one NVIDIA RTX 3090 GPU and the batchsize is 16. |
| Software Dependencies | No | Our model is implemented in Py Torch Imambi et al. (2021). While PyTorch is mentioned, a specific version number is not provided, nor are other key software components with their versions. |
| Experiment Setup | Yes | We use a learning rate of 0.001 to train our model with the Adam optimizer. We set the number of scales in MFAT to 4 and set the dimension of all tokens to 256. In our loss function, we set λec = 1.0, λlmk = 100, λtc = 5.0, λrg = 10.0, λic = 10.0, λpdl = 500.0, λpho = 1.0, λper = 1.0. |