NeRF for scenes that refuse to keep the same shape

What it does

HyperNeRF is the JAX implementation of the paper that extends neural radiance fields to handle topologically varying scenes. It builds directly on JaxNeRF and uses the same dataset format as Nerfies, so existing pipelines transfer over with minimal friction. The codebase provides preset Gin configurations for deformable-surface and axis-aligned-plane experiments, plus separate training and evaluation jobs.

The interesting bit

Instead of warping a fixed radiance field through time, the project lifts the scene into a higher-dimensional space where connectivity changes are expressed natively. That reframe is the paper’s central bet: topology variation becomes tractable when the radiance field itself lives in a higher dimension.

Key highlights

• Built on JAX and JaxNeRF, fitting neatly into Google’s neural-rendering stack.
• Colab notebooks are provided for quick experiments, though the authors explicitly warn these are cut-down models that train slowly and “will likely not be that great.”
• Configuration is handled through Gin, with documented presets for both deformable-surface and axis-aligned-plane setups.
• Uses the same dataset format as Nerfies, so you can reuse existing capture pipelines.
• Training and evaluation run as separate jobs on mutually exclusive GPUs, preventing render passes from stalling the optimizer.

Caveats

• The Colab demos are explicitly basic and low-quality; they are a toy preview, not the full method.
• The README stays high-level, so understanding the actual higher-dimensional representation requires reading the paper.

Verdict

Worth cloning if you’re doing research on dynamic neural rendering and already live in the JAX ecosystem. Give it a pass if you need a polished application; this is a research codebase that expects you to wrangle Gin configs and hardware yourself.