---
tags:
  - robotics
  - anima
  - muninn
  - gaussian-splatting
  - 4dgs
  - dynamic-scenes
  - robot-flow-labs
library_name: pytorch
pipeline_tag: robotics
license: apache-2.0
---

# MUNINN (SD-GS) — ANIMA Wave-6 Module

Part of the [ANIMA Perception Suite](https://github.com/RobotFlow-Labs) by Robot Flow Labs.

## Paper

**SD-GS: Structured Deformable 3D Gaussians for Efficient Dynamic Scene Reconstruction**
Wei Yao, Shuzhao Xie, Letian Li, Weixiang Zhang, Zhixin Lai, Shiqi Dai, Ke Zhang, Zhi Wang
[arXiv:2507.07465](https://arxiv.org/abs/2507.07465) (Jul 2025)

## Architecture

MUNINN implements a **hierarchical deformable anchor grid** for 4D Gaussian Splatting:

- **Deformable Anchor Grid**: 48x48x48 grid (110,592 anchors, 442,368 Gaussians) covering the scene bounding box
- **Spatiotemporal Deformation Field**: MLP predicting per-anchor position/scale/rotation/opacity offsets per frame
- **Anchor-to-Gaussian Derivation**: Each anchor generates 4 local Gaussians via learned offsets
- **CUDA Rasterization**: diff-gaussian-rasterization kernel for real-time rendering

Key results: 60% model size reduction, 100% FPS improvement over dense 4DGS, maintained visual quality.

## D-NeRF Benchmark Results

| Scene | val_loss | Format Sizes |
|-------|----------|-------------|
| bouncingballs | 0.449 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| trex | 0.459 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| hook | 0.469 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| mutant | 0.470 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| lego | 0.471 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| jumpingjacks | 0.475 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| standup | 0.475 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |
| hellwarrior | 0.487 | pth: 40MB, ONNX: 7.7MB, TRT: 7.7MB |

## Exported Formats (per scene)

| Format | File Pattern | Use Case |
|--------|-------------|----------|
| PyTorch (.pth) | `pytorch/muninn_{scene}_v1.pth` | Training, fine-tuning |
| SafeTensors | `pytorch/muninn_{scene}_v1.safetensors` | Fast loading, safe |
| ONNX | `onnx/muninn_{scene}_v1.onnx` | Cross-platform inference |
| TensorRT FP16 | `tensorrt/muninn_{scene}_v1_fp16.trt` | Edge deployment (Jetson/L4) |
| TensorRT FP32 | `tensorrt/muninn_{scene}_v1_fp32.trt` | Full precision inference |

## Usage

```python
import torch
from anima_muninn.core.model import MuninModel

# Load from SafeTensors
from safetensors.torch import load_file
state = load_file("pytorch/muninn_hellwarrior_v1.safetensors")
model = MuninModel(bbox=(...), grid_size=48, ...)
model.load_state_dict(state)
model.eval()

# Render a frame
output = model(poses, intrinsics, times)
rendered_image = output["rendered"]  # (B, 3, 800, 800)
```

## Training

- **Hardware**: 5x NVIDIA L4 (23GB each)
- **Framework**: PyTorch 2.5.1 + CUDA 12.1
- **Grid**: 48x48x48 anchors, 4 Gaussians per anchor
- **Batch size**: 36 (71% VRAM)
- **Optimizer**: Adam (lr=0.0016, gamma=0.95)
- **Early stopping**: patience=200 epochs
- **Config**: See `configs/dnerf.toml`

## ANIMA Stack Position

- **Tier**: 2 (Perception)
- **Wave**: 6
- **Upstream**: THOR (poses), FREYA (point clouds)
- **Downstream**: SURT (occupancy), MAGNI (temporal coherence)
- **Sibling**: HUGINN (complementary 4DGS compression)

## License

Apache 2.0 -- Robot Flow Labs / AIFLOW LABS LIMITED