Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Authors: Zhenguang Liu, Kedi Lyu, Shuang Wu, Haipeng Chen, Yanbin Hao, Shouling Ji2225-2232
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method outperforms state-of-the-art. |
| Researcher Affiliation | Academia | Zhenguang Liu1, Kedi Lyu2 , Shuang Wu3*, Haipeng Chen2*, Yanbin Hao4, Shouling Ji5 1Zhejiang Gongshang University 2Jilin University 3Nanyang Technological University 4University of Science and Technology of China 5Zhejiang University |
| Pseudocode | No | The paper describes the architecture and loss functions in detail but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available at https://github.com/herolvkd/AM-GAN. |
| Open Datasets | Yes | In order to verify the ability of our model, we selected the largest and widely used data set: H3.6m (Ionescu et al. 2014) that contains 3.6 million human images with 3D poses (comprising 25 distinct skeletal joints) obtained via the Vicon mocap system. ... subject 5 is used for testing while the data for the other 6 subjects are used for training. |
| Dataset Splits | No | The paper specifies a training and testing split for the H3.6m dataset (subject 5 for testing, others for training) but does not mention a separate validation split or how validation was performed. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments (e.g., CPU, GPU models, memory, or specific computing cluster details). |
| Software Dependencies | No | The paper states: "We use Tensorflow to implement our method." However, it does not provide a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | The encoder and decoder of VAE consist of a GRU with 1,024 hidden units. ... The aggregation module is a full connected layer with 1,024 units. The local critic is a three-layer fully connected feedforward network with 512 units, while the global critic possesses 1,024 units. The Adam Optimizer (Kingma and Ba 2014) is employed with initial learning 5e-5 and the batch size is set to 16. |