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Creativity unleashed 👨‍🎨🎨✨ 👀 Edify3D from #NVIDIAResearch explores a new way to generate text-to-#3D images. 📗 Project page: 📝 Paper: 🔊 sound on

177,497 次观看 • 1 年前 •via X (Twitter)

10 条评论

NVIDIA AI Developer 的头像
NVIDIA AI Developer1 年前

👨‍🎨 👩‍🎨 ✨ Create your own text-to-#3D images with Edify 3D from #NVIDIAResearch. 📥

The First Skyler-Type Game is moving to Bsky 的头像
The First Skyler-Type Game is moving to Bsky1 年前

its dogshit, as usual great job guys, you're really leading the charge on more terrible products!

Valentin 的头像
Valentin1 年前

You process billions of stolen works from artists around the globe for such garbage - To produce nothing but shit. The fact that you are bending over backwards to hammer a quad retopology over it and advertise the whole thing proves that you have no idea what to do with this garbage. As if it makes a difference whether your completely broken geometry is triangulated or in quads. The output is garbage and completely ethically reprehensible.

Chai 的头像
Chai1 年前

this is not how assets work at all there’s a reason that you guys aren’t game developers or graphic designers

Dario Accornero 的头像
Dario Accornero1 年前

Bloody hell, looks like absolute AI shite…

Paul Lewis 的头像
Paul Lewis1 年前

This shit is garbage.

Saeed Ma'ashi 🐈‍⬛ 的头像
Saeed Ma'ashi 🐈‍⬛1 年前

bruh I prefer making lowpoly models with shitty topology and weak texturing than using your garbage

Ellie @ Simping for a Chicken 🥴😪 的头像
Ellie @ Simping for a Chicken 🥴😪1 年前

Please just stop. This isn’t “creativity”. Creativity would be learning to make it yourself and learning the CREATIVE PROCESS instead of having a soulless machine do it for you. Nothing creative about this.

fichtre 的头像
fichtre1 年前

Stfu. Burning the planet for poorly made 3D shit is despicable

RenegadeHatter(👍) 的头像
RenegadeHatter(👍)1 年前

Scum making scum

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🚨 Paper Alert 🚨 ➡️Paper Title: Articulate3D: Zero-Shot Text-Driven 3D Object Posing 🌟Few pointers from the paper 🎯Authors of this paper proposed a training-free method, “Articulate3D”, to pose a 3D asset through language control. 🎯Despite advances in vision and language models, this task remains surprisingly challenging. 🎯To achieve this goal, they decomposed the problem into two steps. 🎯They modified a powerful image-generator to create target images conditioned on the input image and a text instruction. 🎯They then align the mesh to the target images through a multi-view pose optimisation step. 🎯 In detail, they introduced a self-attention rewiring mechanism (RSActrl) that decouples the source structure from pose within an image generative model, allowing it to maintain a consistent structure across varying poses. 🎯They observed that differentiable rendering is an unreliable signal for articulation optimisation; instead, they used keypoints to establish correspondences between input and target images. 🎯The effectiveness of Articulate3D is demonstrated across a diverse range of 3D objects and free-form text prompts, successfully manipulating poses while maintaining the original identity of the mesh. 🎯Quantitative evaluations and a comparative user study, in which their method was preferred over 85% of the time, confirm its superiority over existing approaches. 🏢Organization: University of Oxford , Google DeepMind 🧙Paper Authors: Oishi Deb, Anjun Hu, Ashkan Khakzar, Philip Torr, Christian Rupprecht 📝 Read the Full Paper here: 🗂️ Project Page: 🎥 Be sure to watch the attached Demo Video - Sound on 🔊🔊 Find this Valuable 💎 ? ♻️QT and teach your network something new Follow me 👣, naveen manwani , for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

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