Blendify -- Python rendering framework for Blender

• URL: http://arxiv.org/abs/2410.17858v1
• Date: Wed, 23 Oct 2024 13:29:02 GMT
• Title: Blendify -- Python rendering framework for Blender
• Authors: Vladimir Guzov, Ilya A. Petrov, Gerard Pons-Moll,
• Abstract summary: Blendify is a Python-based framework that seamlessly integrates with Blender. It automates object creation, handling the colors and material linking, and implementing features such as shadow-catcher objects.
• Score: 31.334130573156937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid growth of the volume of research fields like computer vision and computer graphics, researchers require effective and user-friendly rendering tools to visualize results. While advanced tools like Blender offer powerful capabilities, they also require a significant effort to master. This technical report introduces Blendify, a lightweight Python-based framework that seamlessly integrates with Blender, providing a high-level API for scene creation and rendering. Blendify reduces the complexity of working with Blender's native API by automating object creation, handling the colors and material linking, and implementing features such as shadow-catcher objects while maintaining support for high-quality ray-tracing rendering output. With a focus on usability Blendify enables efficient and flexible rendering workflow for rendering in common computer vision and computer graphics use cases. The code is available at https://github.com/ptrvilya/blendify

Related papers

• SplatBus: A Gaussian Splatting Viewer Framework via GPU Interprocess Communication [1.148281545083889]
  Radiance field-based rendering methods have attracted significant interest from the computer vision and computer graphics communities.<n>They enable high-fidelity rendering with complex real-world lighting effects, but at the cost of high rendering time.<n>3D Splatting solves this issue with a Gaussianisation-based approach for real-time rendering.
  arXiv  Detail & Related papers  (2026-01-21T19:56:22Z)
• Visionary: The World Model Carrier Built on WebGPU-Powered Gaussian Splatting Platform [104.39464309969253]
  We present Visionary, an open, web-native platform for real-time various Gaussian Splatting and rendering.<n> Visionary enables dynamic neural processing while maintaining a lightweight, "click-to-run" browser experience.
  arXiv  Detail & Related papers  (2025-12-09T10:54:58Z)
• MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds [50.98900790623827]
  MeshCoder is a novel framework that reconstructs complex 3D objects from point clouds into editable Blender Python scripts.<n>We train a multimodal large language model (LLM) that translates 3D point cloud into executable Blender Python scripts.<n>Our approach achieves superior performance in shape-to-code reconstruction tasks and also facilitates intuitive geometric and topological editing.
  arXiv  Detail & Related papers  (2025-08-20T17:50:15Z)
• BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing [4.268804603388096]
  We present BlenderGym, the first comprehensive vision-language models (VLMs) system benchmark for 3D graphics editing. We evaluate closed- and open-source VLM systems and observe that even the state-of-the-art VLM system struggles with tasks relatively easy for human Blender users.
  arXiv  Detail & Related papers  (2025-04-02T14:51:45Z)
• Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters [86.13319549186959]
  We present Make-It-Animatable, a novel data-driven method to make any 3D humanoid model ready for character animation in less than one second. Our framework generates high-quality blend weights, bones, and pose transformations. Compared to existing methods, our approach demonstrates significant improvements in both quality and speed.
  arXiv  Detail & Related papers  (2024-11-27T10:18:06Z)
• 3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes [50.36933474990516]
  This work considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance ray tracing hardware. To efficiently handle large numbers of semi-transparent particles, we describe a specialized algorithm which encapsulates particles with bounding meshes. Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision.
  arXiv  Detail & Related papers  (2024-07-09T17:59:30Z)
• Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials [58.178540282148475]
  AssetGen is a significant advancement in text-to-3D generation. It produces faithful, high-quality meshes with texture and material control.
  arXiv  Detail & Related papers  (2024-07-02T17:21:47Z)
• EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction on Mobile Devices [53.28220984270622]
  We present an implicit textured $textbfSurf$ace reconstruction method on mobile devices. Our method can reconstruct high-quality appearance and accurate mesh on both synthetic and real-world datasets. Our method can be trained in just 1-2 hours using a single GPU and run on mobile devices at over 40 FPS (Frames Per Second)
  arXiv  Detail & Related papers  (2023-11-16T11:30:56Z)
• FLARE: Fast Learning of Animatable and Relightable Mesh Avatars [64.48254296523977]
  Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. We introduce FLARE, a technique that enables the creation of animatable and relightable avatars from a single monocular video.
  arXiv  Detail & Related papers  (2023-10-26T16:13:00Z)
• VMesh: Hybrid Volume-Mesh Representation for Efficient View Synthesis [31.52438192597056]
  We present a hybrid volume-mesh representation, VMesh, which depicts an object with a textured mesh along with an auxiliary sparse volume. VMesh can be obtained from multi-view images of an object and renders at 2K 60FPS on common consumer devices with high fidelity.
  arXiv  Detail & Related papers  (2023-03-28T17:49:42Z)
• Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments [8.258451067861932]
  We propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct effects, such as translucent object parts, and preserve object appearance. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects.
  arXiv  Detail & Related papers  (2022-12-12T18:55:03Z)
• VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse [77.52012928882928]
  INDOORKIT is a built-in toolkit for NVIDIA OMNIVERSE. It provides flexible pipelines for indoor scene building, scene randomizing, and animation controls.
  arXiv  Detail & Related papers  (2022-06-23T17:53:33Z)
• VisualEnv: visual Gym environments with Blender [0.0]
  VisualEnv is a new tool for creating visual environment for reinforcement learning. It is the product of an integration of open-source modelling and rendering software, Blender, and a python module used to generate environment model for simulation, OpenAI Gym.
  arXiv  Detail & Related papers  (2021-11-15T21:43:43Z)
• NViSII: A Scriptable Tool for Photorealistic Image Generation [21.453677837017462]
  We present a Python-based built on NVIDIA's OptiX ray tracing engine and the OptiX AI denoiser, designed to generate high-quality synthetic images. Our tool enables the description and manipulation of complex dynamic 3D scenes.
  arXiv  Detail & Related papers  (2021-05-28T16:35:32Z)
• UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments [14.453602631430508]
  We present an improved version of UnrealROX, a tool to generate synthetic data from robotic images. Un UnrealROX+ includes new features such as generating albedo or a Python API for interacting with the virtual environment from Deep Learning frameworks.
  arXiv  Detail & Related papers  (2021-04-23T18:45:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.