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..
Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis
Authors: Zhenhui Ye, Tianyun Zhong, Yi Ren, Jiaqi Yang, Weichuang Li, Jiawei Huang, Ziyue Jiang, Jinzheng He, Rongjie Huang, Jinglin Liu, Chen Zhang, Xiang Yin, Zejun MA, Zhou Zhao
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Real3D-Portrait generalizes well to unseen identities and generates more realistic talking portrait videos compared to previous methods1. 4 EXPERIMENT |
| Researcher Affiliation | Collaboration | Zhejiang University & Byte Dance & HKUST(GZ) |
| Pseudocode | No | The paper describes network structures and training processes but does not include explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | We provide detailed configuration and hyper-parameters in Appendix C, and will release the source code at https://real3dportrait.github.io in the future. |
| Open Datasets | Yes | To train the motion adapter and HTB-SR model, we use a high-fidelity talking face video dataset, Celeb V-HQ (Zhu et al., 2022), which is about 65 hours and contains 35,666 video clips with a resolution of 512 512 involving 15,653 identities. To train the A2M model, we use Vox Celeb2 (Chung et al., 2018), a low-fidelity but 2,000-hour-long large-scale lip-reading dataset to guarantee the generalizability of the audio-to-motion mapping. |
| Dataset Splits | Yes | for the Same-Identity Reenactment, we randomly chose 100 videos in our preserved validation split of Celeb V-HQ. |
| Hardware Specification | Yes | All training processes of Real3D-Portrait are performed on 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions) are mentioned. |
| Experiment Setup | Yes | We provide detailed configuration and hyper-parameters in Appendix C, and will release the source code at https://real3dportrait.github.io in the future. |