ReliaAvatar: A Robust Real-Time Avatar Animator with Integrated Motion Prediction
Authors: Bo Qian, Zhenhuan Wei, Jiashuo Li, Xing Wei
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments 4.1 Implementation Details 4.2 Evaluation Metrics 4.3 Evaluation Protocols 4.4 Comparison to the State-of-the-art 4.5 Ablation Studies |
| Researcher Affiliation | Academia | Bo Qian, Zhenhuan Wei, Jiashuo Li, Xing Wei School of Software Engineering, Xi an Jiaotong University {qb990531, zh-wei, xjtuljs}@stu.xjtu.edu.cn,weixing@mail.xjtu.edu.cn |
| Pseudocode | No | The paper describes the model architecture and training process but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/MIV-XJTU/Relia Avatar. |
| Open Datasets | Yes | We compare Relia Avatar with existing methods in all scenarios on the AMASS benchmark dataset [Mahmood et al., 2019]. Following the experimental setup outlined in Avatar Poser [Jiang et al., 2022], we partition the CMU [graphics lab., 2000], BMLrub [Troje, 2002], and HDM05 [M uller et al., 2007] subsets into 90% training data and 10% testing data. |
| Dataset Splits | No | Section 4.3 "Evaluation Protocols" states: "...we partition the CMU [graphics lab., 2000], BMLrub [Troje, 2002], and HDM05 [M uller et al., 2007] subsets into 90% training data and 10% testing data." This explicitly mentions training and testing data splits but does not specify a validation split. |
| Hardware Specification | Yes | The model is trained on two Ge Force RTX 3090 GPUs for a total of 90000 iterations, with a batch size of 32 on each GPU. All tests are conducted on a single Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper describes the use of GRU and Transformer modules, but it does not specify any software versions for frameworks (e.g., PyTorch, TensorFlow) or programming languages used (e.g., Python version). |
| Experiment Setup | Yes | For all GRUs in the model, the number of layers is set to 1 and the dimension is set to 256. The joint-relation Transformer has 4 layers with a dimension of 512. The initial learning rate is set to 5 10 4, and is halved every 15000 iterations. The length of the input sequence L is set to 32. {λori, λrot, λSMP L pos , λdec pos, λvec} = {0.02, 1, 1, 1, 0.5}. The model is trained on two Ge Force RTX 3090 GPUs for a total of 90000 iterations, with a batch size of 32 on each GPU. |