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project_muninn
ilessio-aiflowlab/project_muninn
project_muninn is a robotics model from ilessio-aiflowlab. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
Part of the ANIMA Perception Suite by Robot Flow Labs.
Access
Public
Updated Mar 29, 2026
Repo size
1.7 GB
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Repo makeup
Click a slice to open those files.
.pth1.3 GB · 71%
At a glance
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Mar 29, 2026
- SHA
- 94105c7d
Model card
From the Hugging Face model README
MUNINN (SD-GS) — ANIMA Wave-6 Module
Part of the ANIMA Perception Suite 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 (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
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
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Created
- Mar 28, 2026
- Updated
- Mar 29, 2026