Hunyuan Part is a specialized 3D AI model from the Tencent Hunyuan ecosystem designed to understand, analyze, and deconstruct complex 3D meshes into meaningful structural components. Instead of generating full objects, it focuses on what happens inside the geometry, identifying parts, boundaries, and functional regions that define how a model is built.
In modern 3D pipelines, raw meshes are often difficult to edit, rig, or reuse. Hunyuan Part solves this by transforming a single unified object into a structured collection of editable components, making it significantly easier to adapt assets for animation, game development, simulation, or digital manufacturing workflows.
Traditional segmentation tools rely heavily on geometric similarity, which often leads to inconsistent or visually inaccurate splits. Hunyuan Part takes a different approach by combining geometric reasoning with learned semantic structure, allowing it to understand what a part represents, not just how it looks.
In practice, this means the model can distinguish between components like handles, limbs, wheels, or mechanical joints even when their shapes are visually similar or partially obscured. This shift from surface-level clustering to semantic decomposition is what makes the system particularly effective in production environments.
The model processes standard 3D formats such as OBJ and GLB, converting them into structured representations that can be directly reused in downstream tools without manual cleanup. According to official documentation, it is optimized for dense mesh inputs and point-based sampling strategies that improve segmentation precision across complex geometry.
Hunyuan Part is designed specifically for real production environments where usability, accuracy, and structural consistency are more important than purely visual results. Its purpose is to support downstream pipelines such as animation, game development, and simulation by turning complex meshes into clean, structured components.
One of the core capabilities of the model is structural decomposition. It begins by analyzing the global shape of a 3D object and then progressively identifies boundaries between meaningful regions within the mesh.
Instead of treating the model as a single continuous surface, it systematically separates it into logically distinct parts. This process ensures that each component reflects a meaningful structural element rather than an arbitrary geometric split.
Beyond geometry, Hunyuan Part incorporates semantic understanding of 3D structure. It does not treat objects as uniform surfaces but instead interprets functional relationships between different regions.
This allows it to consistently identify meaningful components such as limbs, mechanical joints, or structural attachments, even in complex or articulated models. As a result, segmentation remains stable and logically coherent across a wide range of 3D assets.
Hunyuan Part is particularly useful in game development environments where 3D assets need to be modified, reused, or optimized at scale. It reduces the time required to prepare models for rigging and animation by automatically separating functional components of characters and objects, removing much of the manual preprocessing typically required in asset production workflows.
In animation and VFX pipelines, the model provides a more structured way to manage deformation zones. By isolating meaningful parts of a mesh, it becomes easier to apply motion without breaking structural integrity or introducing unwanted distortions.
This is especially important for articulated characters, complex creatures, or mechanical scenes where precise control over movement and deformation is required.
Hunyuan Part is also highly relevant in robotics and simulation contexts, where structured and interpretable 3D data is essential. It converts raw geometry into meaningful, segmented components that can be used for training perception models or simulating real-world interactions.
By bridging the gap between visual representation and machine-readable structure, it helps enable more accurate and scalable AI-driven simulation pipelines.
import requests r = requests.post( "https://api.aimlapi.com/v1/images/generations", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "hunyuan-part", "prompt": "A cat holding a sign that says hello" }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/images/generations", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "hunyuan-part", "prompt": "A cat holding a sign that says hello" }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/images/generations \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"hunyuan-part","prompt":"A cat holding a sign that says hello"}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Output | |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Hunyuan Part This page | Image generation at scale | |||
| Image generation at scale | ||||
| Image generation at scale | ||||
| Image generation at scale | ||||
| Image generation at scale |
Hunyuan Part takes image, text as input and returns image.
Hunyuan Part is priced at $0.052 / gen.
Hunyuan Part is billed per generation — a fixed charge per output rather than by prompt length.
Yes, Hunyuan Part accepts image input as well as a text prompt.
Hunyuan Part was built by Tencent.
Send a request to https://api.aimlapi.com/v1/images/generations with hunyuan-part as the model id.
Yes. Hunyuan Part is served through AI/ML API, so the same key and endpoint format used for other models applies.
