license: apache-2.0
base_model: depth-anything/Video-Depth-Anything-Small
tags:
- depth-estimation
- video
- onnx
- webgpu
library_name: onnxruntime
Video Depth Anything (Small) — ONNX for TheoreticallyPose
Browser-ready ONNX export of Video-Depth-Anything-Small (Apache-2.0), used by the "Consistent (VDA)" depth engine in TheoreticallyPose — a single-file browser app that turns a source clip into pose / depth / silhouette control videos for video-to-video AI generation. The app downloads these two files automatically the first time you use the Consistent engine (~59 MB, cached by your browser).
Files
| file | what it is | input → output |
|---|---|---|
vda_vits_encoder_518x910_fp16.onnx (45 MB) |
per-frame DINOv2-S encoder | [1,3,518,910] normalized RGB → 4 × [1,2405,384] ViT features |
vda_vits_head_T32_37x65_fp16.onnx (14 MB) |
temporal DPT head, 32-frame window | 4 × [32,2405,384] stacked features → [32,1,518,910] depth |
Conversion notes: exported with the PyTorch legacy tracer at opset 17, static shapes (518×910, T=32), split at the ViT-token boundary so per-frame encoder features can be cached across overlapping windows; vanilla attention throughout (no xformers); weights converted to fp16 with fp32 graph I/O. Runs on onnxruntime-web's WebGPU execution provider.
Attribution & license
Original model and architecture: Video Depth Anything by ByteDance
(repo,
paper, CVPR 2025 Highlight).
Only the Small variant is redistributed here, under its original
Apache-2.0 license (see LICENSE). The Base/Large variants are CC-BY-NC and are
not included. This repo contains a format conversion (ONNX/fp16) of the original
checkpoint; no weights were retrained or fine-tuned.
@inproceedings{video_depth_anything,
title={Video Depth Anything: Consistent Depth Estimation for Super-Long Videos},
author={Chen, Sili and Guo, Hengkai and Zhu, Shengnan and Zhang, Feihu and Huang, Zilong and Feng, Jiashi and Kang, Bingyi},
booktitle={CVPR},
year={2025}
}