Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling
Authors: YoungJoong Kwon, Dahun Kim, Duygu Ceylan, Henry Fuchs
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the efficacy of our NIA method, we experiment on ZJU-Mo Cap Peng et al. (2021b) and Mono Cap Habermann et al. (2020; 2021) datasets. First, experiments show that our method outperforms the state-of-the-art Neural Human Performer Kwon et al. (2021) and GP-Ne RF Chen et al. (2022) in novel view synthesis task. Furthermore, we study the more challenging cross-dataset generalization by evaluating the zero-shot performance on the Mono Cap Habermann et al. (2020; 2021) datasets, where we clearly outperform the previous methods. Finally, we evaluate on the pose animation task, where our NIA tested on unseen subjects achieves better pose generalization than the per-subject optimized animatable Ne RF methods. The ablation studies demonstrate that the proposed modules of our NIA collectively contribute to the high-quality rendering for arbitrary human subjects. |
| Researcher Affiliation | Collaboration | Youngjoong Kwon1, Dahun Kim2, Duygu Ceylan3, Henry Fuchs1 1University of North Carolina at Chapel Hill. 2Google Research, Brain Team. 3Adobe Research. |
| Pseudocode | No | The paper describes the methods using mathematical equations and textual descriptions, but it does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | We use ZJU-Mo Cap Peng et al. (2021b) for both tasks and ablation studies. Then we study our cross-dataset generalization ability by training on ZJU-Mocap and testing on Mono Cap datasets without any finetuning. |
| Dataset Splits | Yes | We follow the same training and testing protocols as in Kwon et al. (2021). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or operating systems). |
| Experiment Setup | No | The paper mentions details about the loss function and the number of points sampled for volume rendering ('uniformly sample a set of 64 points'), but it does not provide specific training hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings. |