**Abstract:** SMIRK (Spatial Modeling for Image-based Reconstruction of Kinetics) is a novel method for 3D face reconstruction from images, focusing on accurately capturing expressive facial features. It addresses the limitations of existing methods by improving self-supervised training and enhancing expression diversity. SMIRK replaces the traditional differentiable rendering with a neural rendering module, which generates face images based on the predicted 3D mesh geometry and sparsely sampled pixels from the input image. This approach provides more accurate gradients and allows for the generation of novel expressions during training, effectively augmenting the training data. Extensive experiments demonstrate that SMIRK achieves state-of-the-art performance in reconstructing a wide range of facial expressions, including challenging cases such as asymmetric and subtle movements. **Introduction:** Reconstructing 3D faces from single images has been a central goal in computer vision, with applications in virtual and augmented reality, entertainment, and telecommunication. While existing methods excel at recovering overall face shape, they often fail to capture subtle, extreme, asymmetric, or rarely observed expressions. SMIRK introduces a novel analysis-by-neural-synthesis supervision to improve the quality of reconstructed expressions. By replacing the differentiable rendering step with an image-to-image translator, SMIRK forces the system to rely on the geometry of the predicted mesh, leading to more faithful reconstructions. The method also enables the generation of novel images with varying expressions, enhancing the generalization for diverse expressions. **Related Work:** Recent works have explored various approaches to 3D face reconstruction, including model-free methods, 3D Morphable Models (3DMMs), and deep learning-based methods. However, these methods often suffer from limitations such as the lack of large-scale paired 2D-3D data and the inability to capture complex expressions. SMIRK addresses these issues by combining an analysis-by-synthesis framework with a neural rendering module, which bridges the domain gap between the input and synthesized output, providing a stronger supervision signal. **Method: Analysis-by-Neural-Synthesis:** SMIRK employs a FLAME model to generate 3D geometry and a deep neural network (U-Net) as the encoder to regress FLAME parameters from the input image. The neural renderer, designed to replace traditional graphics-based rendering, uses the predicted geometry and sparsely sampled pixels to generate a face image. This image is then compared to the input image, providing more accurate gradients for the task of expressive 3D face reconstruction. **Results:** SMIRK outperforms existing methods in both quantitative and qualitative evaluations, including emotion recognition accuracy, image reconstruction error, and user studies. The method is particularly effective in capturing complex, asymmetric, and subtle expressions, making it a significant advancement in the field of 3D face reconstruction.**Abstract:** SMIRK (Spatial Modeling for Image-based Reconstruction of Kinetics) is a novel method for 3D face reconstruction from images, focusing on accurately capturing expressive facial features. It addresses the limitations of existing methods by improving self-supervised training and enhancing expression diversity. SMIRK replaces the traditional differentiable rendering with a neural rendering module, which generates face images based on the predicted 3D mesh geometry and sparsely sampled pixels from the input image. This approach provides more accurate gradients and allows for the generation of novel expressions during training, effectively augmenting the training data. Extensive experiments demonstrate that SMIRK achieves state-of-the-art performance in reconstructing a wide range of facial expressions, including challenging cases such as asymmetric and subtle movements. **Introduction:** Reconstructing 3D faces from single images has been a central goal in computer vision, with applications in virtual and augmented reality, entertainment, and telecommunication. While existing methods excel at recovering overall face shape, they often fail to capture subtle, extreme, asymmetric, or rarely observed expressions. SMIRK introduces a novel analysis-by-neural-synthesis supervision to improve the quality of reconstructed expressions. By replacing the differentiable rendering step with an image-to-image translator, SMIRK forces the system to rely on the geometry of the predicted mesh, leading to more faithful reconstructions. The method also enables the generation of novel images with varying expressions, enhancing the generalization for diverse expressions. **Related Work:** Recent works have explored various approaches to 3D face reconstruction, including model-free methods, 3D Morphable Models (3DMMs), and deep learning-based methods. However, these methods often suffer from limitations such as the lack of large-scale paired 2D-3D data and the inability to capture complex expressions. SMIRK addresses these issues by combining an analysis-by-synthesis framework with a neural rendering module, which bridges the domain gap between the input and synthesized output, providing a stronger supervision signal. **Method: Analysis-by-Neural-Synthesis:** SMIRK employs a FLAME model to generate 3D geometry and a deep neural network (U-Net) as the encoder to regress FLAME parameters from the input image. The neural renderer, designed to replace traditional graphics-based rendering, uses the predicted geometry and sparsely sampled pixels to generate a face image. This image is then compared to the input image, providing more accurate gradients for the task of expressive 3D face reconstruction. **Results:** SMIRK outperforms existing methods in both quantitative and qualitative evaluations, including emotion recognition accuracy, image reconstruction error, and user studies. The method is particularly effective in capturing complex, asymmetric, and subtle expressions, making it a significant advancement in the field of 3D face reconstruction.