File size: 2,272 Bytes
9360ac2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
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](https://huggingface.co/depth-anything/Video-Depth-Anything-Small)
(Apache-2.0), used by the **"Consistent (VDA)" depth engine** in
[TheoreticallyPose](https://www.youtube.com/@TheoreticallyMedia) — 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](https://github.com/DepthAnything/Video-Depth-Anything),
[paper](https://arxiv.org/abs/2501.12375), 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.
```bibtex
@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}
}
```
|