AI Directs Visual System for Fashion Editorial

AI at benuts – Ep.2 🌍🎨 In this second episode, we dive into our DMP department and share an internal R&D project. The goal: explore how AI can be used to generate base landscapes, giving our matte painters a strong starting point, while keeping artistic control firmly in human hands. 1️⃣ THE CASTLE SHOT: Starting from a concept created by the DMP Supervisor Isabelle Rousselle, AI was used to generate a first landscape based on her design. From there, multiple versions were explored : weather changes, and even camera movement. Using these AI-generated elements as a foundation, Isabelle then reworked and refined the landscapes to elevate the final result and align it with her artistic vision. 2️⃣ APOCALYPTIC SURVIVORS: Same approach, different world. We began with an AI-generated base shot, carefully guided to achieve the desired atmosphere and tone. Our artists then stepped in to reshape, enhance, and push the visuals further, transforming the AI output into a fully crafted matte painting. 💡In short: AI supports exploration, our artists shape the vision. #AI #generated #prompt #articifial #intelligence #DMP #vfx #vfxstudio #benuts #benutsteam

���🔥Orient Anything V2 is out🔥🔥 👉Orient Anything V2 is a foundation model for unified understanding of object 3D orientation and rotation from single or paired images. V2 extends the capability to handle objects with diverse rotational symmetries & directly estimate relative rotations. Repo under CC-BY-4.0💙 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅Authors: Zhejiang, Shanghai, Sea AI Lab & UHK  ✅Scalable 3D assets synthesized by generative ✅Novel model-in-the-loop annotation system  ✅Symmetry-aware distribution fitting as a L.O. ✅Remarkable zero-shot generalization #AI #deeplearning #computervision #AIwithPapers #metaverse #LLM 👉Discussion https://lnkd.in/dMgakzWm 👉Paper arxiv.org/pdf/2601.05573 👉Project https://lnkd.in/dE6BqS_f 👉Repo https://lnkd.in/dSeQacVA

🎯 Meet DragGAN — a new way to physically control AI-generated images. Instead of rewriting prompts again and again, DragGAN lets you literally drag any point in an image to where you want it. Pose, shape, expression, layout — all under direct control. ✨ What makes it powerful: • 🖱️ Drag points to precise target positions • 🧩 Deform images with pixel-level control • 🧠 Works across categories — humans, animals, cars, landscapes • 🎨 Maintains realism, rigidity & structure Because it operates inside the GAN’s learned image manifold, results stay realistic — even when: • Filling missing or hidden parts • Reshaping objects • Handling complex deformations 🚀 This feels like moving from prompting to direct manipulation of generative images. #AI #DragGAN #GenerativeAI #ComputerVision #ImageEditing #AIResearch #DesignTools #Innovation

A recent study ran a 'visual telephone' game with AI image generators for 100,000 total iterations. The result wasn't random chaos. It was convergence onto just 12 generic visual styles. Researchers at 𝘗𝘢𝘵𝘵𝘦𝘳𝘯𝘴 subjected models like Stable Diffusion XL to a feedback loop. A detailed prompt generated an image, which another AI described, and that description generated the next image. This loop ran for up to 100 rounds per chain, repeated 1,000 times. The original, imaginative prompts didn't drift randomly. They collapsed into a narrow set of predictable motifs: 🏖️ Maritime lighthouses 🏠 Rustic architecture 🌃 Urban nightscapes 🏛️ Formal interiors This convergence happened regardless of the starting prompt or the specific models used. The authors call these outputs 'visual elevator music'—bland, generic, and lacking true creativity. The study suggests a core limitation: despite vast training data, models are biased toward familiar, marketable scenes from human-curated datasets. They default to a safe, repetitive visual palate. This isn't just about aesthetics. It's a data-driven argument about the current ceiling for generative AI 'creativity.' The system optimizes for recognition, not innovation. What does this mean for the future of AI art? Is this a fundamental architectural limit, or just a phase in model development? #AI #GenerativeAI #MachineLearning #CreativeAI 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/gN32eCdW

Three images. One machine. Zero AI. The drawing, the solid model, and the realistic render all come from a single, fully controlled source, created entirely by human skill. This loop shows how industrial design can stay honest across every representation, from concept to visualization, without relying on AI. #IndustrialDesign #DesignAccuracy #CAD #3DModeling #3DRendering #ProductVisualization #ManualSkill #Manufacturing

WSQ - Introduction to AI tools for content creation offered by FirstCom Academy introduces content creation for image, video and text using Leonardo.AI, Lumen-5 and ChatGPT. Although targeting the marketing sector, it provides the fundamentals for further advancement in Generative AI. I would highly recommend this course for all working professionals who wish to gain fundamental knowledge and skills in generative AI for their work.

Precision in AI Filmmaking: The "Ingredients" Method. 🎬 Maintaining character and environmental consistency remains one of the greatest challenges in generative video. At Beyond Edge, we solve this by treating the AI pipeline like a traditional production. In this breakdown, we demonstrate the power of Veo 3.1 when anchored by specific Midjourney assets. By "feeding" the model high-fidelity references for the environment and character, we eliminate the randomness of raw prompting. Key Technical Takeaways: Asset Anchoring: Using pre-generated images as the blueprint for video physics. Narrative Control: Directing specific elements (like floating papers) without losing character integrity. Professional Scalability: A workflow designed for consistency across multiple shots. We are entering a new era where we don't just prompt — we design. #BeyondEdge #GenerativeAI #VideoProduction #WorkflowInnovation #FutureOfCinema #AI #Midjourney #Veo3

Day 2 – Generative AI Live Class | Color Shading in Art (Gamma AI Presentation) As part of Day 2 of my Generative AI Live Class, I created a 10-slide presentation on Color Shading in Art using Gamma AI. This activity helped me understand how AI tools can be used to structure creative topics, design visually engaging slides, and present concepts in a professional format. Key highlights of the presentation: Fundamentals of color shading in art Understanding light, shadow, and depth Use of shading to enhance realism and visual impact Structured and visually balanced slides created with Gamma AI Key takeaway: Gamma AI makes it easier to transform creative ideas into clear, well-designed presentations, saving time while maintaining quality. 🔗 Presentation link shared in the comments #GenerativeAI #GammaAI #LiveClass #ArtAndDesign #ColorShading #CreativeLearning #AItools #LearningJourney

What makes this moment powerful is not the pose. It is the precision behind it. Timing, balance, lighting, and motion converge into a single frame where nothing feels accidental. This is the same shift we are seeing with AI right now. The value is no longer raw output. It is orchestration. When systems understand context, sequence, and intent, results feel inevitable rather than engineered. This is why tools like computer vision pipelines built on OpenCV, pose estimation frameworks such as MediaPipe, and diffusion based image systems like Stable Diffusion with ControlNet are changing creative and technical work. These models do not just generate images. They reason about structure. Skeletal alignment, depth estimation, lighting vectors, temporal coherence. When AI respects constraints, creativity scales without losing integrity. The real takeaway for all of us is that mastery still matters. AI does not replace the discipline behind the shot, the movement, or the decision. It amplifies it when the foundations are strong. When human intuition meets models trained on feedback, physics, and context, automation disappears and trust takes its place. That is where technology feels natural. 🎥 VC: viral_art #ArtificialIntelligence #ComputerVision #CreativeTechnology

Split4D: Decomposed 4D Scene Reconstruction Without Video Segmentation https://lnkd.in/e-sZCunC This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representation through differentiable rendering techniques. Therefore, they heavily rely on the quality of video segmentation maps, which are often unstable, leading to unreliable reconstruction results. To overcome this challenge, our key idea is to represent the decomposed 4D scene with the Freetime FeatureGS and design a streaming feature learning strategy to accurately recover it from per-image segmentation maps, eliminating the need for video segmentation. Freetime FeatureGS models the dynamic scene as a set of Gaussian primitives with learnable features and linear motion ability, allowing them to move to neighboring regions over time. We apply a contrastive loss to Freetime FeatureGS, forcing primitive features to be close or far apart based on whether their projections belong to the same instance in the 2D segmentation map. As our Gaussian primitives can move across time, it naturally extends the feature learning to the temporal dimension, achieving 4D segmentation. Furthermore, we sample observations for training in a temporally ordered manner, enabling the streaming propagation of features over time and effectively avoiding local minima during the optimization process. Experimental results on several datasets show that the reconstruction quality of our method outperforms recent methods by a large margin. --- Newsletter https://lnkd.in/emCkRuA More story https://lnkd.in/eMFcEekQ LinkedIn https://lnkd.in/ehrfPYQ6 #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning #ComputerVision