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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Generalizable One-shot 3D Neural Head Avatar
Authors: Xueting Li, Shalini De Mello, Sifei Liu, Koki Nagano, Umar Iqbal, Jan Kautz
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed approach generalizes well to unseen validation datasets, surpassing SOTA baseline methods by a large margin on head avatar reconstruction and animation. |
| Researcher Affiliation | Industry | Xueting Li, Shalini De Mello, Sifei Liu, Koki Nagano, Umar Iqbal, Jan Kautz https://research.nvidia.com/labs/lpr/one-shot-avatar |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://research.nvidia.com/labs/lpr/one-shot-avatar |
| Open Datasets | Yes | Training datasets. We train our model using a single-view image dataset (FFHQ [26]) and two video datasets (Celeb V-HQ [74] and RAVDESS [37]). |
| Dataset Splits | No | The paper mentions using training datasets (FFHQ, Celeb V-HQ, RAVDESS) and evaluation datasets (Celeb A, HDTF testing split), but does not specify the exact train/validation/test splits (e.g., percentages or sample counts) for all datasets used in their own training process to allow full reproduction of the data partitioning. |
| Hardware Specification | Yes | We implement the proposed method using the Py Torch framework [45] and train it with 8 32GB V100 GPUs. |
| Software Dependencies | No | The paper mentions software like "Py Torch framework [45]", "Seg Former [59]", and "GFPGAN [58; 51]" but does not specify their version numbers, which are necessary for reproducible dependency descriptions. |
| Experiment Setup | Yes | The first training stage takes 6 days, consisting of 750000 iterations. The second training stage takes 2 days with 75000 iterations. Both stages use a batch size of 8 with the Adam optimizer [30] and a learning rate of 0.0001. |