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VGGT-1B-Depth is a hosted version of Visual Geometry Grounded Transformer (VGGT) that estimates scene depth and related 3D information from still images or video. On Replicate, you can upload images or a clip, run the model, and inspect depth images, per-image JSON data, and optionally a 3D point-cloud visualization. It is an estimate of scene geometry—not a guaranteed metrically accurate scan.
What VGGT-1B-Depth does
VGGT stands for Visual Geometry Grounded Transformer. The upstream project describes VGGT as a feed-forward neural network that predicts scene geometry from one or more views. Depending on the model version, predictions can include camera parameters, depth maps, point maps, and 3D point tracks. The paper describes these predictions as being made in a single forward pass.
The Replicate model vufinder/vggt-1b-depth is a particular depth-oriented implementation. Its listing says it uses VGGT-1B-Commercial weights and omits the point-map head because the depth head makes that head redundant in this variant. Its outputs therefore should not be assumed to match every output exposed by the broader VGGT project. See the upstream VGGT repository and the CVPR 2025 paper for the project and model background.
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The Replicate listing accepts still images in JPG, JPEG, PNG, and WEBP formats, as well as MP4, AVI, and MOV video. You can submit multiple files. For video, a sampling-rate setting determines how often frames are selected; the listing says the first and last frames are always included. Images and sampled frames are padded to a common aspect ratio and resized so their maximum dimension is 518 pixels. These are documented preprocessing behaviors, not additional controls for choosing the resize limit.
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For a useful first experiment, choose a small set of related views of the same scene or a short clip with clear visual overlap between frames. The model predicts geometry from the supplied views, so unrelated pictures are not a meaningful multi-view scene.
What the outputs mean
- Depth images: visualizations of estimated distance structure in each image. They are useful for inspecting relative scene shape, but should not be treated as verified measurements.
- JSON data: per-image numerical results, including the image, pose encoding, depth, depth confidence, original image dimensions, and optionally a mask. The listing spells one dimension field as
heigth; that appears to be a schema typo. - GLB point cloud: an optional downloadable 3D file containing a point cloud and camera meshes, which you can open in a compatible 3D viewer to inspect the scene visualization.
The listing also exposes settings related to base64-encoded JSON arrays, alpha-channel blending, and excluding selected output keys. The exact output schema and options can change, so check the live model listing when you run it.
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How to try it on Replicate
- Open the VGGT-1B-Depth model page and use its web interface.
- Upload one or more supported images, or a supported video. For video, set the frame sampling rate to suit the clip and the amount of detail you want to inspect.
- Leave the depth-image output enabled if you want visual results; enable point-cloud output if you want a GLB scene visualization. Review other output settings only if you need their specific formats or filtering behavior.
- Run the model, then open or download the resulting depth images and JSON files. If returned, download the GLB file and open it in a compatible 3D viewer.
The model page also documents programmatic examples for Python, Node.js, and HTTP. API use requires a Replicate token; keep it private and do not place it in public code or share it. The page’s model version and displayed cost can change, so use the current listing rather than relying on copied version identifiers or cost figures.
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| Route | When it fits | What to consider |
|---|---|---|
| Replicate hosted model | You want to try the named depth variant without setting up the model runtime locally. | Inputs are processed through the hosted service; cost is an approximate, input-dependent estimate shown on the listing. Its current version and options are listed on the model page. |
| Upstream local workflow | You want to work directly with the upstream project and manage your own software environment. | The repository documents Python inference using PyTorch, torchvision, NumPy, Pillow, and Hugging Face Hub dependencies. This is a developer workflow, not a one-click setup; the cited project materials do not establish a minimum GPU specification for beginners. |
The paper describes VGGT as reconstructing from one, a few, or hundreds of views and characterizes its predictions as occurring “in seconds.” That is a general description in the paper, not a runtime guarantee for every input or for this hosted deployment. If you are choosing between routes, consider how much setup control you need, which output you will use, the number of images or sampled video frames, the hosted service’s current input-dependent cost, and the checkpoint’s license.
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Check the checkpoint license before commercial use
The Replicate listing identifies its weights as VGGT-1B-Commercial. The upstream repository’s July 29, 2025 README update says the commercial checkpoint permits commercial use subject to its license, while the original checkpoint remains non-commercial. Check the exact checkpoint and read the current VGGT license before commercial deployment or redistribution; the license text governs the applicable terms.
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