ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
Authors: Francesca Babiloni, Alexandros Lattas, Jiankang Deng, Stefanos Zafeiriou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess the efficacy of ID-to-3D as a specialized method for ID-driven expressive human face generation in different scenarios and report comparative analysis against state-of-the-art text-to-3D and image-to-3D generation pipelines. Further analysis, implementation details and discussion can be found in additional material. Quantitative Comparisons: FID and User-Study. The evaluation of the generated 3D geometries and textures is performed using the Frechet Inception Distance (FID) metric [28]. |
| Researcher Affiliation | Academia | Francesca Babiloni, Alexandros Lattas, Jiankang Deng, Stefanos Zafeiriou Imperial College London, UK |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We base our method on public code, report our technical details in the supplemental materials, and will be releasing our code. We use already publicly available data, and will be open-sourcing our code. |
| Open Datasets | Yes | To overcome the need for a large-scale dataset, we leverage a small dataset of human heads with different expressions (NHPM) [24] |
| Dataset Splits | Yes | We include all the above details in the supplemental materials. |
| Hardware Specification | Yes | We have included the above details in the supplemental materials. |
| Software Dependencies | Yes | We have included the above details in the supplemental materials. |
| Experiment Setup | Yes | We include all the above details in the supplemental materials. |