The GitHub repository titled "LightAvatar-TensorFlow" is dedicated to the codebase for a workshop paper presented at ECCV 2024, titled "LightAvatar: Efficient Head Avatar as Dynamic Neural Light Field." The project is spearheaded by a team of researchers from various institutions, including Northeastern University, Google, and Simon Fraser University. The lead author, Huan Wang, can be contacted for inquiries related to the project. LightAvatar serves as a proof-of-concept framework that utilizes neural light field (NeLF) technology to create photorealistic 3D head avatars. The system operates by taking 3D Morphable Model (3DMM) parameters and camera poses as inputs, allowing it to render RGB images through a single network forward pass, eliminating the need for mesh inputs. This innovative approach enables the rendering of 512x512 images at an impressive speed of 174 frames per second on a consumer-grade GPU, specifically the RTX 3090, using standard deep learning frameworks. The code provided in the repository is primarily based on TensorFlow. However, due to intellectual property constraints from Google, the complete code cannot be released. Instead, the repository includes key modules related to the model architecture and loss functions for reference. Users are encouraged to reach out with any questions or issues they may encounter. The authors also invite users to cite their work if it proves beneficial, providing a citation format for academic referencing. The repository is tagged with various topics related to the project, including avatar creation, knowledge distillation, neural rendering, and digital human representation, highlighting its relevance in the fields of computer graphics and artificial intelligence. Overall, the LightAvatar project represents a significant advancement in the efficient rendering of dynamic 3D avatars, showcasing the potential of neural light fields in creating realistic digital representations.