Training 3D generative models often faces bottle-necks due to dependencies on precise camera pose estimation, particularly when using Neural Radiance Fields (N eRFs) for photorealistic novel-view synthesis. We introduce HyperNeR-FGAN, a generative framework that eliminates camera pose requirements by integrating a hypernetwork with a Generative Adversarial Network (GAN). This architecture maps Gaussian noise directly to the weights of a NeRF model, bypassing viewing direction inputs during training. Our experiments demonstrate that HyperNeRFGAN achieves state-of-the-art performance on datasets where camera position estimation is impractical - notably in medical imaging scenarios with limited or ambiguous viewpoint metadata. Despite its architectural simplicity compared to existing methods, the model produces high-fidelity 3D reconstructions across diverse modalities, including MRI and X-ray-derived 2D scans. The framework's efficiency and robustness suggest broad applicability in domains requiring 3D generation from unstructured or poorly annotated 2D data. Key revisions emphasize the camera-pose independence, clinical relevance, and architectural efficiency while maintaining technical nrecision.

Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multilayer perceptrons (MLPs). However, MLPs are known to exhibit a low-frequency bias, limiting their ability to capture high-frequency details accurately. This limitation is typically addressed by incorporating high-frequency input embeddings or specialized activation layers. In this work, we demonstrate that these embeddings and activations are often configured with hyperparameters that perform well on average but are suboptimal for specific input signals under consideration, necessitating a costly grid search to identify optimal settings. Our key observation is that the initial frequency spectrum of an untrained model's output correlates strongly with the model's eventual performance on a given target signal. Leveraging this insight, we propose frequency shifting (or FreSh), a method that selects embedding hyperparameters to align the frequency spectrum of the model’s initial output with that of the target signal. We show that this simple initialization technique improves performance across various neural representation methods and tasks, achieving results comparable to extensive hyperparameter sweeps but with only marginal computational overhead compared to training a single model with default hyperparameters.