hansen1416 / video2motion-train

Network Architecture:

Concatenate: Combine the ResNet features and 3D joint positions tensor: combined_input = torch.cat([features, joint_positions], dim=1) Initial Dense Layer: Pass the combined input through a dense layer with a moderate number of neurons (e.g., 256). Use ReLU activation for non-linearity. Recurrent or Attention Layers: Consider one of the following approaches: Recurrent layers (LSTM or GRU): If the order of joints or temporal context is important, use LSTM or GRU layers stacked on top of the initial dense layer. This captures sequential information from the joint positions. Attention mechanism: For focusing on specific joints or relationships between them, use a self-attention or graph attention layer after the initial dense layer. This allows the model to learn important connections between joints. Additional Dense Layers: Add one or two more dense layers with ReLU activation to learn higher-level representations. Output Layer: Use a final dense layer with the number of outputs equal to 4 times the number of joints, corresponding to the quaternion representation of joint rotations. Consider using tanh activation to constrain the output between -1 and 1, or you can convert the output to Euler angles if preferred. Loss Function:

Use a mean squared error (MSE) loss function to compare the predicted and actual quaternion values. You can also explore alternative loss functions suitable for angular data like angle error or geodesic loss. Optimizer:

Start with a standard optimizer like Adam or RMSprop, but fine-tune hyperparameters (learning rate, batch size) based on your specific task and dataset. Additional Considerations:

Normalization: Normalize the ResNet features and 3D joint positions to avoid large values dominating the learning. Data Augmentation: Apply random rotations, translations, and scaling to the 3D joint positions to improve generalization. Regularization: Use techniques like dropout or L1/L2 regularization to prevent overfitting. Implementation Tips:

Use PyTorch Lightning or other frameworks for easier implementation and experiment management. Visualize the attention weights if you use an attention mechanism to understand which joints influence the prediction. Evaluate your model on different metrics relevant to your application, such as average angle error or the ability to reconstruct realistic poses.

https://github.com/josharnoldjosh/Resnet-Extract-Image-Feature-Pytorch-Python/blob/master/extract_image_feature.py

https://pytorch.org/hub/pytorch_vision_resnet/

You're right, an encoder-decoder attention model is a good starting point for your task of predicting joint rotations based on features and positions. Here are some hybrid structures you can consider to enhance performance:

1. Concatenate Features and Positions:

Combine the image features and 3D joint positions before feeding them into the encoder. This allows the encoder to learn a joint representation considering both visual context and spatial information. You can concatenate directly or project them to the same dimensionality before concatenating. 2. Multi-Input Encoder:

Use separate encoder branches for the image features and joint positions. These branches can be LSTMs, CNNs, or any suitable architecture. Then, either: Concatenate the outputs of the branches before the decoder. Use attention between the encoder outputs to let each branch attend to relevant information from the other. 3. Attention on Encoder Hidden States:

Use attention within the encoder itself, where one encoder branch attends to the hidden states of the other while processing information. This allows the information to flow within the encoder, leveraging context from both sources. 4. Auxiliary Decoder Branch:

Add a separate decoder branch for predicting intermediate information related to the image features. This branch can predict object locations, limb lengths, or other relevant aspects from the image, which can then be used in the main decoder branch for predicting rotations. Additional Tips:

Experiment with different architectures for each branch (LSTMs, CNNs, transformers). Consider using residual connections within the network to improve gradient flow. Employ batch normalization and dropout for regularization. Evaluate your model using appropriate metrics like average angular error or pose reconstruction fidelity. Visualize the attention weights to understand how the model focuses on different parts of the input data. Remember, the best structure depends on your specific dataset and task. Don't hesitate to experiment, evaluate, and refine your model to achieve optimal performance.

By incorporating these hybrid approaches, you can leverage the information from both image features and joint positions, potentially leading to improved accuracy in your joint rotation prediction task.