<p>In this article we create a simple, yet robust pipeline for image to 3D mesh generation with detection grounding using Qwen3-VL, BiRefNet, and Hunyuan3D 2.0 model.</p>
<p>The post <a href="https://debuggercafe.com/image-to-3d-mesh-generation-with-detection-grounding/">Image to 3D Mesh Generation with Detection Grounding</a> appeared first on <a href="https://debuggercafe.com">DebuggerCafe</a>.</p>

تم تطوير خط أنابيب جديد لتوليد الشبكات ثلاثية الأبعاد من الصور، باستخدام نماذج Qwen3-VL وBiRefNet وHunyuan3D 2.0، مما يعزز قدرات الكشف في تطبيقات الذكاء الاصطناعي. تم تفصيل هذا التقدم في مقال حديث من DebuggerCafe.

Se ha desarrollado un nuevo pipeline para la generación de mallas 3D a partir de imágenes, utilizando los modelos Qwen3-VL, BiRefNet y Hunyuan3D 2.0, mejorando así las capacidades de detección en aplicaciones de IA. Este avance se detalla en un reciente artículo de DebuggerCafe.

Un nouveau pipeline pour la génération de maillages 3D à partir d'images a été développé, utilisant les modèles Qwen3-VL, BiRefNet et Hunyuan3D 2.0, améliorant ainsi les capacités de détection dans les applications d'IA. Cette avancée est détaillée dans un récent article de DebuggerCafe.

A new pipeline for image to 3D mesh generation has been developed, utilizing the Qwen3-VL, BiRefNet, and Hunyuan3D 2.0 models, enhancing the capabilities of detection grounding in AI applications. This advancement is detailed in a recent article by DebuggerCafe.

Image to 3D Mesh Generation with Detection Grounding

arXiv:2601.08489v1 Announce Type: new 
Abstract: Safety-aligned language models systematically refuse harmful requests. While activation steering can modulate refusal, ablating the raw "refusal vector" calculated from contrastive harmful and harmless prompts often causes collateral damage and distribution drift. We argue this degradation occurs because the raw vector is polysemantic, entangling the refusal signal with core capability circuits and linguistic style.
  We introduce Surgical Refusal Ablation (SRA) to distill these steering directions. SRA constructs a registry of independent Concept Atoms representing protected capabilities and stylistic confounds, then uses ridge-regularized spectral residualization to orthogonalize the refusal vector against these directions. This yields a clean refusal direction that targets refusal-relevant structure while minimizing disruption to the model's semantic geometry.
  Across five models (Qwen3-VL and Ministral series), SRA achieves deep refusal reduction (0-2%) with negligible perplexity impact on Wikitext-2 (mean delta PPL approx. 0.02) and minimal distribution drift. Notably, standard ablation on Qwen3-VL-4B induces severe drift (first-token KL = 2.088), whereas SRA maintains the original distribution (KL = 0.044) while achieving the same 0% refusal rate. Using teacher-forced perplexity on GSM8K and MBPP as a high-resolution capability proxy, we show SRA preserves math and code distributions. These results suggest that common "model damage" is often "Ghost Noise," defined as the spectral bleeding of the dirty refusal direction into capability subspaces.

تهدف مقدمة عملية الاستئصال الجراحي للرفض (SRA) إلى تعزيز أمان نماذج اللغة من خلال تحسين قدراتها على الرفض، مما يقلل من الأضرار الجانبية والانحراف في التوزيع الناتج عن الأساليب التقليدية. تحقق SRA ذلك من خلال إنشاء سجل من الذرات المفاهيمية المستقلة واستخدام التصفية الطيفية المنتظمة بواسطة ridge لإنتاج اتجاه رفض واضح.

La introducción de la Ablación Quirúrgica de Rechazo (SRA) tiene como objetivo mejorar la seguridad de los modelos de lenguaje al refinar sus capacidades de rechazo, minimizando los daños colaterales y la deriva de distribución causados por métodos tradicionales. La SRA logra esto creando un registro de Átomos de Concepto independientes y utilizando la residualización espectral regularizada por ridge para producir una dirección de rechazo clara.

L'introduction de l'Ablation de Refus Chirurgical (SRA) vise à améliorer la sécurité des modèles de langage en affinant leurs capacités de refus, minimisant les dommages collatéraux et la dérive de distribution causés par les méthodes traditionnelles. Le SRA y parvient en créant un registre d'Atomes de Concept indépendants et en utilisant la résidualisation spectrale régularisée par crête pour produire une direction de refus claire.

The introduction of Surgical Refusal Ablation (SRA) aims to enhance the safety of language models by refining their refusal capabilities, minimizing collateral damage and distribution drift caused by traditional methods. SRA achieves this by creating a registry of independent Concept Atoms and utilizing ridge-regularized spectral residualization to produce a clean refusal direction.

Image to 3D Mesh Generation with Detection Grounding

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