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..
ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment
Authors: Yicheng Zhong, Huawei Wei, Peiji Yang, Zhisheng Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments illustrate that our method accomplishes expressive facial animation generation and offers enhanced flexibility in effectively conveying the desired style. |
| Researcher Affiliation | Industry | 1 Tencent Technology (Shenzhen) Co.Ltd EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MEAD is a talking-face video corpus featuring 60 actors talking with 8 different emotions at 3 different intensity levels. ... (Wang et al. 2020) ... BEAT comprises 76 hours of speech data, paired with 52D facial blendshape weights. ... (Liu et al. 2022) |
| Dataset Splits | No | For TEAD and MEAD-3D, the paper states, 'We use 90% of the data for training and the remaining 10% for testing'. It does not specify a separate validation split or its size for any of the datasets used. |
| Hardware Specification | Yes | The entire framework is trained using the Adam optimizer (Kingma and Ba 2014) on a single A100 GPU. |
| Software Dependencies | No | The paper mentions 'Our framework is implemented by Pytorch(Paszke et al. 2019)' but does not provide specific version numbers for PyTorch or other key libraries used (e.g., CLIP-Vi TB/32, Adam optimizer). |
| Experiment Setup | Yes | Exp CLIP is trained with a learning rate of 1e-5 and a batch size of 256. ... An 8-layer transformer decoder is used... Each training sample has a duration of 64 frames with FPS=15. |