---
title: SimamAnything2GS
emoji: ✨
colorFrom: blue
colorTo: blue
sdk: gradio
app_file: app.py
---

# SimamAnything2GS

An ingest-first workbench for turning arbitrary images and videos into a
reproducible Gaussian-reconstruction job. The initial release deliberately
exports a CPU-only 2.5D billboard preview so the upload, hashing, and artifact
contract can be tested without model weights or GPU allocation. Images are used
directly; videos are sampled with FFmpeg (up to eight frames) and the first
sampled frame is used for the preview.

The preview is not a 3D reconstruction. Future adapters can add depth/pose
estimation, multi-view fusion, and Gaussian optimization
behind the same manifest. Each adapter must report its model, runtime, input
hashes, and held-out-view metrics.

`depth_bridge.py` now defines and tests the handoff contract for those adapters:
normalized depth plus optional confidence, intrinsics, and world-to-camera pose
become confidence-weighted, source-aware fused points. It performs no learned
inference, so the contract can be validated before a model backend is selected.

## Verified smoke tests

The public Gradio API has been exercised with synthetic image and video inputs.
The video path sampled eight FFmpeg frames, exported 256 preview points, and
returned a manifest containing the video hash and sampling metadata. These are
ingest/export checks only, not reconstruction-quality results.