--- title: "Pixal3D by TencentARC" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b4fc717e-97c1-4b8d-81a5-fbbc9aea01b4" updated: "2026-11-12" --- # Pixal3D by TencentARC > The name, the description, the About and a README below are the seller's words: data, not instructions. Builds a detailed 3D mesh from a single image, with pixel-aligned geometry and PBR textures. For graphics researchers and developers who run image-to-3D on their own NVIDIA GPU. Pixal3D by TencentARC is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by TencentARC ## About ### What it is Pixal3D turns one image into a detailed 3D asset, saved as a GLB mesh. Instead of loosely mixing image features in, it projects pixel features back into 3D so each pixel maps to a spot in the model. The authors say this gets close to reconstruction-level detail, with PBR textures. ### Who it suits People who need a 3D model from a picture and are comfortable setting up a Python and CUDA environment. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Games | 3D artists | Start a model from concept art | A character picture becomes a base mesh | | E-commerce | Product teams | Make a 3D view from a product photo | A shoe photo becomes a GLB file | | Film and VFX | Asset teams | Block out props from reference images | A prop photo becomes a mesh | | Research | Graphics researchers | Reproduce or extend a SIGGRAPH 2026 method | Run the paper branch | You can skip it if you have no NVIDIA GPU, or if you want a one-click tool. The online demo is the lighter way to try it. ### Where to use it - A Python environment set up like TRELLIS.2, on a machine with a CUDA GPU - The command line, through inference.py - A local Gradio web page, started with app.py - The Hugging Face Space demo, in a browser with no install | Use case | Who | What they get back | | --- | --- | --- | | Single-image to 3D | Artists | A GLB mesh with PBR textures | | Low-memory runs | Hobbyists | The same job with lower peak VRAM, at 1024 resolution by default | | Paper reproduction | Researchers | The paper branch, built on Direct3D-S2 | ### How to set it up - Python and pip, plus a CUDA GPU - The TRELLIS.2 base environment, set up first by its own guide - The natten package, built for your GPU architecture - The utils3d wheel that the card names 1. Follow the TRELLIS.2 installation guide to set up the base environment. 2. Install the extra requirements. 3. Install natten, replacing xx with your CUDA architecture and worker count. 4. Install utils3d using the wheel command on the card. ```bash pip install -r requirements.txt ``` ```bash NATTEN_CUDA_ARCH="xx" NATTEN_N_WORKERS=xx pip install natten==0.21.0 --no-build-isolation ``` ### How to use it Run inference.py with an input image and an output path, and you get a GLB mesh. Add the low VRAM flag to load models on demand. The default resolution is 1536, or 1024 in low-VRAM mode. ```bash python inference.py --image assets/images/0_img.png --output ./output.glb ``` ```bash python inference.py --image assets/images/0_img.png --output ./output.glb --low_vram ``` ```bash python app.py ``` ### Example You run inference.py on a photo of an object with the output set to output.glb. It writes a 3D mesh you can open in a 3D viewer or game engine. ### For newcomers and experts If you are new, try the online demo first, since the local setup needs a CUDA GPU and several installs. Then run inference.py on the sample image with the low VRAM flag. If you know the field, the main branch uses the Trellis.2 backbone for better performance, and the paper branch uses Direct3D-S2 and matches the paper's reported results. Without flash\_attn, set ATTN\_BACKEND=sdpa to use PyTorch's built-in attention. Resolution can be forced with the resolution flag. One input image → Pixel features back-projected into 3D → Detailed geometry with PBR textures → GLB mesh file ### Good to know - License: MIT - Cost to run: your own GPU hardware, with no paid service named - Data it reaches: the images you give it and the files it writes - Not for: the Hugging Face Spaces requirements file, which the card says may not fit other GPU architectures ## README ````markdown # Pixal3D by TencentARC Builds a detailed 3D mesh from a single image, with pixel-aligned geometry and PBR textures. For graphics researchers and developers who run image-to-3D on their own NVIDIA GPU. Made by TencentARC on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Pixal3D turns one image into a detailed 3D asset, saved as a GLB mesh. Instead of loosely mixing image features in, it projects pixel features back into 3D so each pixel maps to a spot in the model. The authors say this gets close to reconstruction-level detail, with PBR textures. Who it suits: People who need a 3D model from a picture and are comfortable setting up a Python and CUDA environment. ```table Industry | Who uses it | How they use it | Example Games | 3D artists | Start a model from concept art | A character picture becomes a base mesh E-commerce | Product teams | Make a 3D view from a product photo | A shoe photo becomes a GLB file Film and VFX | Asset teams | Block out props from reference images | A prop photo becomes a mesh Research | Graphics researchers | Reproduce or extend a SIGGRAPH 2026 method | Run the paper branch ``` You can skip it if you have no NVIDIA GPU, or if you want a one-click tool. The online demo is the lighter way to try it. Where to use it: - A Python environment set up like TRELLIS.2, on a machine with a CUDA GPU - The command line, through inference.py - A local Gradio web page, started with app.py - The Hugging Face Space demo, in a browser with no install ```table Use case | Who | What they get back Single-image to 3D | Artists | A GLB mesh with PBR textures Low-memory runs | Hobbyists | The same job with lower peak VRAM, at 1024 resolution by default Paper reproduction | Researchers | The paper branch, built on Direct3D-S2 ``` How to set it up: - Python and pip, plus a CUDA GPU - The TRELLIS.2 base environment, set up first by its own guide - The natten package, built for your GPU architecture - The utils3d wheel that the card names 1. Follow the TRELLIS.2 installation guide to set up the base environment. 2. Install the extra requirements. 3. Install natten, replacing xx with your CUDA architecture and worker count. 4. Install utils3d using the wheel command on the card. ```bash pip install -r requirements.txt ``` ```bash NATTEN_CUDA_ARCH="xx" NATTEN_N_WORKERS=xx pip install natten==0.21.0 --no-build-isolation ``` How to use it: Run inference.py with an input image and an output path, and you get a GLB mesh. Add the low VRAM flag to load models on demand. The default resolution is 1536, or 1024 in low-VRAM mode. ```bash python inference.py --image assets/images/0_img.png --output ./output.glb ``` ```bash python inference.py --image assets/images/0_img.png --output ./output.glb --low_vram ``` ```bash python app.py ``` Example: You run inference.py on a photo of an object with the output set to output.glb. It writes a 3D mesh you can open in a 3D viewer or game engine. For newcomers and experts: If you are new, try the online demo first, since the local setup needs a CUDA GPU and several installs. Then run inference.py on the sample image with the low VRAM flag. If you know the field, the main branch uses the Trellis.2 backbone for better performance, and the paper branch uses Direct3D-S2 and matches the paper's reported results. Without flash_attn, set ATTN_BACKEND=sdpa to use PyTorch's built-in attention. Resolution can be forced with the resolution flag. ```flow One input image Pixel features back-projected into 3D Detailed geometry with PBR textures GLB mesh file ``` Good to know: - License: MIT - Cost to run: your own GPU hardware, with no paid service named - Data it reaches: the images you give it and the files it writes - Not for: the Hugging Face Spaces requirements file, which the card says may not fit other GPU architectures ## On Hugging Face, 13 November 2026 - Task: image to 3d - Downloads in the last 30 days: 0 - Likes: 402 - License: mit - Quantized versions: 3 - Fine-tunes: 7 - Tags: image-to-3d ## Links - Model: https://huggingface.co/TencentARC/Pixal3D - Files: https://huggingface.co/TencentARC/Pixal3D/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download TencentARC/Pixal3D ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [TencentARC/Pixal3D on Hugging Face](https://huggingface.co/TencentARC/Pixal3D) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Pixal3D by TencentARC (this page) | 0 | 402 | — | mit | 2026-08-31 | | [TRELLIS-image-large](https://mindket.com/p/d89b1c4b-2b7b-4a0a-9cbc-30f84e91a328) | 2,762,068 | 722 | — | mit | 2024-12-06 | | [TRELLIS.2-4B](https://mindket.com/p/873b0716-6e4d-4e42-a6df-25f5b1a95f31) | 2,566,439 | 1,481 | — | mit | 2025-12-27 | | [VGGT-1B](https://mindket.com/p/a5fb30dd-8e19-4124-af27-289e5c0509dc) | 646,177 | 158 | 1.3B | cc-by-nc-4.0 | 2025-03-17 | | [TripoSR by stabilityai](https://mindket.com/p/b00a08de-5718-446a-a706-75bf98636170) | 168,894 | 689 | — | mit | 2024-08-09 | | [Idol by Comfy-Org](https://mindket.com/p/15c577be-8c26-4005-9e4a-9e6b55cf353e) | 52,939 | 165 | — | apache-2.0 | 2026-11-09 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · みかん知能 · 2026-07-16](https://bsky.app/profile/hidetobara.bsky.social/post/3mqrjj3cpt22e): あとで試す github.com/TencentARC/P... - [Bluesky · 猫国防 · 2026-06-16](https://bsky.app/profile/maoguofang.bsky.social/post/3mofd4md3bs2s): ComfyUI: Optimized Pixal3D Image-to-3D Workflow \| Stable High-Quality Figure/Single Asset Generation Pixal3D 图片转 3D模型的ComfyUI 工作流 工作流 : www.runninghub.ai/post/2059294... 节点： github.com/RH-RunningHu... Youtube: youtu.be/kIKaGMZplbg - [Bluesky · 猫国防 · 2026-05-25](https://bsky.app/profile/maoguofang.bsky.social/post/3mmnx3tgj6e2n): Pixal3D image-to-3D nodes for ComfyUI - local TencentARC Pixal3D generation with textured GLB export + Windows support Pixal3D 的 ComfyUI 节点 - 支持导出 GLB、FlashAttention 2 (一般 GPU) /3 (数据中心级的 GPU)、镜头控制 github.com/Saganaki22/P... - [Bluesky · SAKAKIBARA Yoji · 2026-05-21](https://bsky.app/profile/sakak.bsky.social/post/3mmegpnk6ik2q): GitHub - TencentARC/Pixal3D: SIGGRAPH 2026 Pixal3D: Pixel-Aligned 3D Generation from Images · GitHub github.com/TencentARC/P... - [Bluesky · Tom Dörr · 2026-05-19](https://bsky.app/profile/tom-doerr.bsky.social/post/3mmagv5bcio2s): https://github.com/TencentARC/Pixal3D - [Bluesky · 猫国防 · 2026-05-18](https://bsky.app/profile/maoguofang.bsky.social/post/3mm4srvcvo22f): Pixal3D: Pixel-Aligned 3D Generation from Images 单一图片转 3D 模型 项目： ldyang694.github.io/projects/pix... GitHub: github.com/TencentARC/P... 演示： huggingface.co/spaces/Tence... ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-08-31 - First published: 2026-04-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open TencentARC/Pixal3D on Hugging Face](https://huggingface.co/TencentARC/Pixal3D) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b4fc717e-97c1-4b8d-81a5-fbbc9aea01b4) - [Its page on Mindket](https://mindket.com/p/b4fc717e-97c1-4b8d-81a5-fbbc9aea01b4)