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 [1].
XAGen: 3D Expressive Human Avatars Generation
Authors: Zhongcong XU, Jianfeng Zhang, Jun Hao Liew, Jiashi Feng, Mike Zheng Shou
NeurIPS 2023 | Venue PDF | 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 EMAIL Jianfeng Zhang Byte Dance EMAIL Jun Hao Liew Byte Dance EMAIL Jiashi Feng Byte Dance EMAIL Mike Zheng Shou Show Lab National University of Singapore EMAIL |
| 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. |