A newer version of the Gradio SDK is available: 6.30.0
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.