XAGen: 3D Expressive Human Avatars Generation
Authors: Zhongcong XU, Jianfeng Zhang, Jun Hao Liew, Jiashi Feng, Mike Zheng Shou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that XAGen surpasses state-of-the-art methods in terms of realism, diversity, and expressive control abilities. We conduct extensive experiments on a variety of benchmarks [18, 68, 14, 36], demonstrating the superiority of XAGen over state-of-the-arts in terms of appearance, geometry, and controllability. |
| Researcher Affiliation | Collaboration | Zhongcong Xu Show Lab National University of Singapore zhongcongxu@u.nus.edu Jianfeng Zhang Byte Dance jianfengzhang@bytedance.com Jun Hao Liew Byte Dance junhao.liew@bytedance.com Jiashi Feng Byte Dance jshfeng@bytedance.com Mike Zheng Shou Show Lab National University of Singapore mike.zheng.shou@gmail.com |
| Pseudocode | No | The paper describes the method using diagrams and mathematical formulas but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | Code and data will be made available at https://showlab.github.io/xagen. |
| Open Datasets | Yes | We evaluate the performance of XAGen on four datasets, i.e., Deep Fashion [36], MPV [68], UBC [14], and SHHQ [18]. |
| Dataset Splits | No | The paper states it uses training data but does not explicitly provide specific proportions or counts for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running experiments. |
| Software Dependencies | No | The paper mentions using a 'pretrained model [17]' for SMPL-X parameter estimation but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | No | The paper describes the components of the loss function and relative sizes of Tri-planes (e.g., Wf = Wh = Wb/2) but does not provide specific hyperparameter values like learning rates, batch sizes, number of epochs, or the numerical values for the weighting factors (λ) in the loss terms. |