OxiGAF 0.1.0 Released — Pure Rust Gaussian Avatar Reconstruction from Monocular Videos via Multi-View Diffusion

A major leap in digital humans for the COOLJAPAN ecosystem.

On February 24 we released OxiGAF 0.1.0 — a complete pure-Rust implementation of Gaussian Avatar Reconstruction from monocular videos using multi-view diffusion.

This is the first production-ready Rust implementation of the GAF method (arXiv:2412.10209) that turns a casual selfie video into a high-fidelity, animatable 3D Gaussian avatar — all without multi-camera rigs, heavy Python dependencies, or unsafe code.

Why OxiGAF is revolutionary

Traditional avatar reconstruction (e.g., Gaussian Splatting papers, NeRF-based methods) relies on:

• Multi-view capture setups
• Python + PyTorch + CUDA-heavy stacks
• Slow training (hours to days)

OxiGAF changes the game:

• Single monocular video input only
• 100% Pure Rust (no C/C++/Fortran)
• Differentiable rendering via wgpu (GPU on every platform)
• Real-time inference capable
• Seamless integration with oxihuman (parametric body) and oximedia (video pipelines)

Technical Deep Dive: The GAF Pipeline in Rust

The core innovation combines three pillars:

1. Multi-View Diffusion (oxigaf-diffusion)

   • UNet-based diffusion model that generates 4–8 consistent views from a single input frame.
   • IP-Adapter for identity preservation + classifier-free guidance (scale 1.0–20.0).
   • Cross-view attention + explicit camera pose embedding.
   • Latent upsampler (32→64) for high-quality 512×512 outputs.

2. Differentiable Gaussian Splatting (oxigaf-render)

   • CPU reference rasterizer + full GPU implementation via wgpu.
   • Gradient-verified 35 tests (relative error < 1e-3).
   • Supports opacity, spherical harmonics (SH degree 3), covariance scaling, and rotation.

3. FLAME Parametric Binding (oxigaf-flame)

   • Linear Blend Skinning (LBS) + normal maps.
   • Safetensors I/O + video sequence caching with LRU.
   • Bidirectional PyTorch ↔ OxiGAF weight conversion via oxigaf-bridge.

The full training loop verifies gradients end-to-end, ensuring the entire pipeline is differentiable and optimizable.

What’s inside 0.1.0

• Full GAF pipeline (diffusion → Gaussian optimization → FLAME binding)
• 796 passing tests (100% coverage)
• Feature flags: cuda, metal, simd, flash_attention, mixed_precision
• CLI + Python bindings ready
• Zero unwrap() in critical paths

This is the foundation

OxiGAF is now the avatar reconstruction layer for the entire COOLJAPAN stack:

• oxihuman + OxiGAF = parametric body + photorealistic face & hair from video
• oximedia pipes reconstructed avatars into real-time video streams
• mielinOS agents get photorealistic digital twins
• oxilean can formally verify avatar physics and animation constraints

Repository: https://github.com/cool-japan/oxigaf

Star the repo if you want digital humans that are fast, safe, and truly sovereign.

The era of “upload to a cloud service for avatar reconstruction” is over.
Pure Rust Gaussian avatars are here — and they run everywhere.

— KitaSan at COOLJAPAN OÜ
February 24, 2026