Video Depth Estimation in the Browser with WebGPU and Depth Anything V2

Standard video formats record color and motion across a two-dimensional grid, leaving spatial context—specifically how far each object is from the camera lens—implicit. By bringing client-side depth estimation directly to web applications using WebGPU and Depth Anything V2 Small, developers can extract spatial maps in real time without backend server latency or cloud computing costs.

Integrating browser-local depth estimation transforms flat RGB frames into actionable 3D layers. This enables client-side rendering capabilities such as depth-aware bokeh blurs, dynamic relighting, edge-guided masking, and independent grayscale depth video exports.

In this technical guide, we will break down the end-to-end architecture for client-side video depth estimation, cover timing synchronization logic, implement WebGPU pipelines, and evaluate how depth maps perform as control inputs for downstream AI video generation tools.

Understanding Monocular Depth Estimation

Monocular depth estimation models like Depth Anything V2 Small predict structural distance from single RGB images. Unlike calibrated stereo vision setups, relative depth outputs map visual geometry onto an uncalibrated scale:

• Brighter Pixels: Represent surfaces closer to the camera focal plane.
• Darker Pixels: Represent distant background objects.

Relative depth maps do not state absolute metric distance (e.g., "an object is exactly 2.4 meters away"). However, they provide sufficient spatial ordering for visual compositing, masking, and motion tracking.

+-----------------------+      +--------------------------+      +-------------------------+
| Raw Video Frame (RGB) | ---> |  Web Worker (WebGPU)     | ---> | Relative Depth Map      |
| (Decode & Resample)   |      |  Depth Anything V2 Q4F16 |      | (Normalized Grayscale)  |
+-----------------------+      +--------------------------+      +-------------------------+

The Browser Processing Pipeline

To keep video playback smooth and avoid UI lockups, all heavy inference and frame analysis tasks must run off the main thread within dedicated Web Workers.

The complete end-to-end processing sequence follows six main steps:

1. Timeline Mapping: Map global timeline time to the underlying video asset's source media time.
2. Frame Decoding & Resampling: Extract the visual frame at the target time and resize it to model input bounds.
3. Worker Transfer: Send frame data to a Web Worker via OffscreenCanvas or transferred ArrayBuffers.
4. Inference Execution: Run Depth Anything V2 via WebGPU utilizing 4-bit/16-bit quantized weights (q4f16).
5. Post-Processing & Filtering: Normalize output tensors and apply edge-guided bilateral smoothing.
6. Rendering & Compositing: Render real-time shaders for depth blur or encode to WebM depth video streams.

Implementing Depth Anything V2 with WebGPU

Using @huggingface/transformers (Transformers.js), setting up WebGPU execution in a Web Worker requires configuring quantized execution parameters for fast execution.

Web Worker Model Loader (depthWorker.js)

import \{ pipeline, env \} from "@huggingface/transformers";

// Disable local model checks if downloading from a CDN mirror
env.allowLocalModels = false;

let estimator = null;

async function initPipeline(modelPath = "onnx-community/depth-anything-v2-small") \{
  if (!estimator) \{
    estimator = await pipeline("depth-estimation