✨Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects

📝 Summary:
Kinematify is an automated framework that synthesizes high-DoF articulated objects from images or text. It infers kinematic topologies and estimates joint parameters, combining MCTS search with geometry-driven optimization for physically consistent models.

🔹 Publication Date: Published on Nov 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01294
• PDF: https://arxiv.org/pdf/2511.01294

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✓ https://xn--r1a.website/DataScienceT

#3DModeling #ComputerVision #Robotics #AIResearch #Kinematics

✨NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modeling

📝 Summary:
NURBGen generates high-fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines NURBS. It fine-tunes an LLM to translate text into NURBS parameters, enabling robust modeling with a hybrid representation. NURBGen outperforms existing text-to-CAD methods in geometric fidelity ...

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06194
• PDF: https://arxiv.org/pdf/2511.06194

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✓ https://xn--r1a.website/DataScienceT

#TextToCAD #LLM #NURBS #3DModeling #GenerativeAI

✨MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts

📝 Summary:
MajutsuCity is a language-driven framework for generating 3D urban scenes, offering high structural consistency, stylistic diversity, and controllability. It uses a four-stage pipeline and an interactive editing agent, significantly outperforming existing methods.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20415
• PDF: https://arxiv.org/pdf/2511.20415
• Project Page: https://longhz140516.github.io/MajutsuCity/
• Github: https://github.com/LongHZ140516/MajutsuCity

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#GenerativeAI #3DModeling #CityGeneration #ComputerGraphics #DeepLearning

✨ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion

📝 Summary:
ActionMesh extends 3D diffusion models with a temporal axis to generate high-quality, rig-free animated 3D meshes. This 'temporal 3D diffusion' framework quickly creates topology-consistent animations from various inputs like video or text, achieving state-of-the-art results.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16148
• PDF: https://remysabathier.github.io/actionmesh/actionmesh_2026.pdf
• Project Page: https://remysabathier.github.io/actionmesh/
• Github: https://github.com/facebookresearch/actionmesh

🔹 Models citing this paper:
• https://huggingface.co/facebook/ActionMesh

✨ Spaces citing this paper:
• https://huggingface.co/spaces/facebook/ActionMesh

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#3DAnimation #DiffusionModels #ComputerGraphics #DeepLearning #3DModeling

🔥 PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects

💡 The paper introduces PhysX-Omni, a unified framework for generating simulation-ready 3D assets with physical properties across multiple categories. The problem addressed is that existing 3D generation methods either neglect physical properties or are limited to a single asset category, such as rigid, deformable, or articulated objects. To address this, the authors develop a novel geometry representation tailored for vision-language models, which directly encodes high-resolution 3D structures without compression, significantly improving generation performance.

The PhysX-Omni framework generates simulation-ready physical 3D assets using this novel geometry representation. The authors also construct the first general simulation-ready 3D dataset, PhysXVerse, covering diverse indoor and outdoor categories. To evaluate the framework, they propose PhysX-Bench, a benchmark that encompasses six key attributes: geometry, absolute scale, material, affordance, kinematics, and function description.

The results show that PhysX-Omni performs strongly in both generation and understanding, outperforming conventional metrics and PhysX-Bench. Additional studies validate the potential of PhysX-Omni for applications in simulation-ready scene generation and robotic policy learning. The authors believe that PhysX-Omni can significantly advance a wide range of downstream applications, particularly in embodied AI and physics-based simulation.

The key contributions of the paper are the development of a novel geometry representation, the construction of the PhysXVerse dataset, and the proposal of the PhysX-Bench benchmark. These contributions enable the generation of simulation-ready physical 3D assets across multiple categories, which can be used in various applications such as robotics, computer vision, and simulation. Overall, the paper presents a significant advancement in the field of 3D generation and simulation, with potential applications in a wide range of areas.

📅 Published on May 20

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.21572
• PDF: https://arxiv.org/pdf/2605.21572
• Project Page: https://physx-omni.github.io

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📢 By: https://xn--r1a.website/PaperNexus

#ComputerVision #3DModeling #PhysicsBasedSimulation #ArticulatedObjectSimulation #DeformableObjectModeling