TripoSplat is a fast AI image-to-3D Gaussian splat generation model that converts a single image into a high-quality 3D Gaussian splat file with PLY and SPLAT export options. Ready-to-use REST inference API for 3D reconstruction, Gaussian-splat workflows, product visualization, AR/VR content, game assets, digital twins, and professional 3D asset generation with simple integration, no coldstarts, and affordable pricing.
Idle
$0.05per run·~20 / $1
Tripo3D TripoSplat Image-to-3D converts a single input image into a 3D Gaussian splat, suitable for fast view synthesis, lightweight 3D previews, and splat-based rendering workflows. It supports adjustable Gaussian count, inference steps, guidance strength, and export format selection.
Single-image 3D splat generation
Turn one image into a 3D Gaussian splat without requiring multi-view capture.
Adjustable splat density
Control num_gaussians to balance detail and file size.
Tunable generation quality
Use num_inference_steps and guidance_scale to control fidelity and adherence to the source image.
Flexible export formats
Export as ply or splat depending on your downstream workflow.
Production-ready API
Useful for 3D previews, splat rendering, rapid prototyping, and lightweight scene reconstruction workflows.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Input image to convert into a 3D Gaussian splat. |
| num_gaussians | No | Target number of Gaussians in the output splat. Higher values can preserve more detail but create larger files. Range: 32768–262144. Default: 262144. |
| num_inference_steps | No | Number of flow-matching sampler steps. More steps can improve fidelity with higher runtime. Range: 1–50. Default: 20. |
| guidance_scale | No | Classifier-free guidance strength. Higher values follow the input image more strongly but may oversaturate colors. Range: 0–10. Default: 3. |
| output_format | No | Output Gaussian splat file format. Supported values: ply, splat. Default: ply. |
num_inference_steps and guidance_scale as needed.ply or splat.Convert a product image into a 3D Gaussian splat for quick preview and interactive rendering.
Just $0.05 per generation.
num_gaussians, num_inference_steps, guidance_scale, and output_format do not affect pricingnum_gaussians for smaller files and faster downstream handling.num_inference_steps when you want more fidelity and can tolerate longer runtime.guidance_scale carefully if the output starts to look oversaturated or too tightly constrained.image is required.num_gaussians defaults to 262144.num_inference_steps defaults to 20.guidance_scale defaults to 3.output_format defaults to ply.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/tripo3d/triposplat/image-to-3d with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Triposplat Image To 3d below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"num_gaussians": 262144,
"num_inference_steps": 20,
"guidance_scale": 3,
"output_format": "ply"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/tripo3d/triposplat/image-to-3d" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/tripo3d/triposplat/image-to-3d";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"num_gaussians": 262144,
"num_inference_steps": 20,
"guidance_scale": 3,
"output_format": "ply"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"num_gaussians": 262144,
"num_inference_steps": 20,
"guidance_scale": 3,
"output_format": "ply"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/tripo3d/triposplat/image-to-3d", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Triposplat Image To 3d is a Tripo3D model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. TripoSplat is a fast AI image-to-3D Gaussian splat generation model that converts a single image into a high-quality 3D Gaussian splat file with PLY and SPLAT export options. Ready-to-use REST inference API for 3D reconstruction, Gaussian-splat workflows, product visualization, AR/VR content, game assets, digital twins, and professional 3D asset generation with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/tripo3d/tripo3d-triposplat-image-to-3d.
Triposplat Image To 3d starts at $0.050 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `image`, `guidance_scale`, `num_inference_steps`, `num_gaussians`, `output_format`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/tripo3d/tripo3d-triposplat-image-to-3d.
Median end-to-end generation time on WaveSpeedAI is around 22 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (Tripo3D). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.