What is HY-3D-UV?
HY-3D-UV is a specialized model in Tencent’s HY-3D family. Its purpose is to automate UV unwrapping for existing 3D models. UV unwrapping converts the surface of a three-dimensional mesh into a two-dimensional layout, commonly called a UV map. Artists and production tools use that layout to place textures on the model without unwanted stretching, overlaps, or gaps.
Rather than creating a mesh from a written prompt or an image, HY-3D-UV starts with an uploaded or remotely hosted 3D file. Tencent documents support for FBX, OBJ, and GLB input files. The service processes the model and produces generated 3D assets, including OBJ and FBX outputs. It may also return an image resource associated with the completed task.
This makes HY-3D-UV best understood as a 3D asset-preparation service. It addresses a specific production step that can otherwise require manual work in 3D software. It is not a language model and does not produce text, audio, video, or images as its primary model output.
Where HY-3D-UV fits in Tencent’s catalog
Tencent currently makes HY-3D-UV available through Tencent Cloud TokenHub. The documented API identifier is hy-3d-uv, and Tencent’s model catalog classifies it as a UV-unwrapping service with a default concurrency of one task.
Its position in the catalog is distinct from general-purpose language and multimodal models. HY-3D-UV does not accept ordinary text prompts as its main input and does not return token-based answers. Its role is closer to an automated processing stage in a digital-content pipeline: a system sends a supported 3D asset, waits for processing, and retrieves the resulting files.
Supported inputs and output assets
The verified input formats are FBX, OBJ, and GLB. The TokenHub request uses a 3D model URL rather than a conversational prompt. Tencent documents the following input constraints:
- Maximum input file size: 60 MB.
- Maximum mesh complexity: 30,000 faces.
- Maximum number of components or connected regions: 100.
These limits are important when deciding whether an existing asset can be sent directly to the service. A large production model may need to be simplified, divided into smaller assets, or exported in a supported format before processing. The supplied documentation does not specify a maximum number of output tokens because HY-3D-UV is not a text-generating model. It also does not provide a fixed output polygon count or a detailed guarantee about every output file’s contents.
According to Tencent’s documentation, generated assets can include OBJ and FBX files, and a task may also return an image resource. The exact output should therefore be treated as a processed asset package rather than a textual response.
What HY-3D-UV does well
The model’s main strength is specialization. UV unwrapping is a concrete, repeatable operation, but it can become time-consuming when many assets must be prepared for texturing. HY-3D-UV provides an automated route for supported meshes, potentially reducing manual preparation in workflows that process large numbers of models.
Its file-based interface is also practical for pipeline integration. A content-management system, asset service, or batch preparation tool can provide a model URL, submit a task, and collect the processed result. This is more suitable for automated asset handling than a general AI chat interface would be.
The model is particularly relevant when the input asset already exists but lacks a usable UV layout. Examples include:
- Preparing imported meshes for texture artists.
- Processing 3D assets for rendering or visualization.
- Adding an automated preparation step to game-development pipelines.
- Converting or organizing assets for digital-content production.
- Testing an automated alternative before assigning manual UV work to an artist.
These are workflow benefits rather than benchmark claims. The supplied research does not include independent quality measurements comparing HY-3D-UV with manual unwrapping or other commercial tools.
Important limitations and trade-offs
HY-3D-UV’s narrow scope is both its defining advantage and its main limitation. It cannot replace a model-generation system when the starting point is a text description, reference image, or concept. It also is not intended for text generation, image generation, video generation, audio processing, general-purpose reasoning, or conversational assistance.
The input restrictions may also exclude complex assets. A file above 60 MB, a mesh with more than 30,000 faces, or an asset containing more than 100 connected regions does not fit the documented limits without preprocessing. Users may need to reduce geometry, separate components, or export a smaller version first. Such changes can affect the asset and may require validation before the result is used in production.
Tencent documents a default concurrency of one task. This means the service should not automatically be assumed to support high-volume parallel processing under the default configuration. Teams planning a large asset queue should confirm applicable account, quota, and concurrency options with Tencent rather than designing around unverified throughput assumptions.
The available research also does not specify a guaranteed processing time, output-quality score, texture-resolution limit, retention period, or detailed failure-handling behavior. Those details should be verified in the current TokenHub documentation before production deployment.
Pricing and cost position
Tencent’s pricing documentation lists HY-3D-UV at 10 points per call. Tencent states that one point is worth CNY 0.12, making the documented unit price equivalent to CNY 1.20 per call before any account-specific terms, taxes, discounts, or other charges. The pricing is usage-based rather than a recurring monthly model plan, so the cost depends primarily on the number of processing calls.
At that listed rate, the service can be attractive for targeted automation where each call replaces a meaningful amount of manual preparation. However, cost comparisons should include preprocessing, failed jobs, storage, review, and any downstream artist work. A low per-call price does not guarantee that every returned UV layout will be production-ready without inspection.
The research does not provide a separate input or output token price, because HY-3D-UV does not operate as a token-generating language model. It also does not document a batch discount or a separate high-throughput price.
Modalities, reasoning, and tool support
HY-3D-UV accepts 3D model files through a model URL. In the supplied model classification, it supports 3D asset input rather than text, image, audio, or video input. Its direct output is a processed 3D asset, with possible image-resource output documented by Tencent. This is a file-processing capability, not image generation in the usual image-model sense.
There is no documented conversational reasoning mode, chain-of-thought feature, or general planning capability. The model also has no listed tool or function-calling support. An application can call the TokenHub service as part of a larger workflow, but that should not be confused with the model autonomously selecting tools or executing functions.
Likewise, HY-3D-UV is not a coding model. It does not generate or analyze software code as its primary task. Any code used to submit jobs or retrieve output belongs to the surrounding application integration, not to the model’s output capability.
When to choose HY-3D-UV
Choose HY-3D-UV when you already have a supported 3D asset and need an automated UV-unwrapping step. It is a sensible option for teams that want to connect model preparation to an existing content pipeline, especially when the assets fit Tencent’s documented limits and a per-call pricing model is convenient.
It is less appropriate when you need to create a new 3D mesh, generate an asset from text or images, edit UV seams interactively, perform detailed artistic optimization, or process large and complex files beyond the published limits. In those cases, a dedicated desktop 3D package, a specialized manual or automated UV tool, or a different 3D-generation service may be more suitable.
HY-3D-UV should also be evaluated differently from general AI models. Its value is not measured by language reasoning, coding benchmarks, or image quality. The relevant questions are whether it accepts the project’s file formats and mesh complexity, whether the resulting UV layout meets the texturing requirements, and whether the per-call cost and default concurrency fit the production schedule.
Bottom line
HY-3D-UV is a focused Tencent service for turning supported 3D files into UV-unwrapped assets. Its documented strengths are automated processing, integration through Tencent Cloud TokenHub, support for common 3D formats, and a clear usage price of 10 points per call. Its limitations are equally clear: it is not a general AI assistant or 3D generator, it has a 60 MB and 30,000-face input ceiling, and its default concurrency is one task.
For a pipeline that repeatedly receives manageable FBX, OBJ, or GLB assets, HY-3D-UV can serve as a practical automated preparation stage. For creative modeling, complex meshes, or hands-on UV art direction, it should be treated as a narrowly useful processing component rather than a complete 3D production solution.