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📢ScanEdit: Hierarchically-Guided Functional 3D Scan Editing Edit complex, real-world 3D scans with text -- Mohamed El Amine Boudjoghra combines LLM reasoning with geometric optimization to produce physically plausible, instruction-aligned scene edits Check it out:

Angela Dai

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12,987 views • 1 year ago •via X (Twitter)

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3D editing is hard: you need to ground an image + instruction and generate a faithful 3D shape in one forward pass -- no test-time optimization. So, we steer pretrained image-to-3D representations to do text-guided 3D edits; no massive 3D edit-pair dataset needed. Key trap: the “no-edit” solution is a nasty local minimum. We fix it with preference optimization, pushing the model to actually edit. Steer3D is the second work that adapts alignment ideas from LLMs to the 3D modality. SAM 3D also used DPO to improve its 3D generations.

3D editing is hard: you need to ground an image + instruction and generate a faithful 3D shape in one forward pass -- no test-time optimization. So, we steer pretrained image-to-3D representations to do text-guided 3D edits; no massive 3D edit-pair dataset needed. Key trap: the “no-edit” solution is a nasty local minimum. We fix it with preference optimization, pushing the model to actually edit. Steer3D is the second work that adapts alignment ideas from LLMs to the 3D modality. SAM 3D also used DPO to improve its 3D generations.

Georgia Gkioxari

116,061 views • 7 months ago

📢ProcGen3D: Learning Neural Procedural Graphs for Image-to-3D Reconstruction Xinyi Zhang learns neural procedural graphs to generate high-fidelity 3D - MCTS-guided sampling maintains consistency with the input image, even from real images! Check it out:

📢ProcGen3D: Learning Neural Procedural Graphs for Image-to-3D Reconstruction Xinyi Zhang learns neural procedural graphs to generate high-fidelity 3D - MCTS-guided sampling maintains consistency with the input image, even from real images! Check it out:

Angela Dai

14,639 views • 8 months ago

🥁 The winners of the Google Maps Platform Awards are now live, and the Grand Prize goes to Innobrix! 🎉 The 3D configuration platform combines BIM data with Google Maps to provide real-world context for developments. 📢 Check it out along with the rest of our winners ⬇️

🥁 The winners of the Google Maps Platform Awards are now live, and the Grand Prize goes to Innobrix! 🎉 The 3D configuration platform combines BIM data with Google Maps to provide real-world context for developments. 📢 Check it out along with the rest of our winners ⬇️

Google Maps Platform

14,361 views • 10 months ago

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

Matthias Niessner

23,087 views • 7 months ago

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

AK

62,768 views • 3 years ago

Deep Dive Video: Complex image editing used to take hours — now Google's Gemini 2.0 turns advanced ComfyUI & Photoshop workflows into simple text prompts. Here's exactly how to try it (completely free). Chapters: 00:00 Conversational Editing with Google's Multimodal AI 00:53 Image Generation w/ LLM World Knowledge 02:12 Easy Image Editing & Colorization 02:46 Advanced Conversational Edits (Chaining Prompts Together) 03:37 Long Text Generation (Google Beats OpenAI To The Punch) 04:25 Making Spicy Memes (Google AI Studio Safety Settings) 05:48 Advanced Prompting (One Shot ComfyUI Workflows) 07:19 Re-posing Characters (While Keeping Likeness Intact) 08:27 Spatial 3D Understanding (NO ControlNet) 10:42 Semantic Editing & In/Out Painting 13:46 Sprite Sheets & Animation Keyframes 14:40 Using Gemini To Build Image Editing Apps 16:37 Making Videos w/ Conversational Editing

Deep Dive Video: Complex image editing used to take hours — now Google's Gemini 2.0 turns advanced ComfyUI & Photoshop workflows into simple text prompts. Here's exactly how to try it (completely free). Chapters: 00:00 Conversational Editing with Google's Multimodal AI 00:53 Image Generation w/ LLM World Knowledge 02:12 Easy Image Editing & Colorization 02:46 Advanced Conversational Edits (Chaining Prompts Together) 03:37 Long Text Generation (Google Beats OpenAI To The Punch) 04:25 Making Spicy Memes (Google AI Studio Safety Settings) 05:48 Advanced Prompting (One Shot ComfyUI Workflows) 07:19 Re-posing Characters (While Keeping Likeness Intact) 08:27 Spatial 3D Understanding (NO ControlNet) 10:42 Semantic Editing & In/Out Painting 13:46 Sprite Sheets & Animation Keyframes 14:40 Using Gemini To Build Image Editing Apps 16:37 Making Videos w/ Conversational Editing

Bilawal Sidhu

34,755 views • 1 year ago

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,105 views • 7 months ago

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

TurboEdit Instant text-based image editing discuss: We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and the reconstructed image from the previous step, allowing for correction of the next reconstruction towards the input image. We demonstrate that disentangled controls can be easily achieved in the few-step diffusion model by conditioning on an (automatically generated) detailed text prompt. To manipulate the inverted image, we freeze the noise maps and modify one attribute in the text prompt (either manually or via instruction based editing driven by an LLM), resulting in the generation of a new image similar to the input image with only one attribute changed. It can further control the editing strength and accept instructive text prompt. Our approach facilitates realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion (one-time cost) and 4 NFEs per edit. Our method is not only fast, but also significantly outperforms state-of-the-art multi-step diffusion editing techniques.

AK

16,062 views • 1 year ago

Wow. That's cool. Researchers just released World, an open-source Unreal Engine 5 simulator for training and testing LLM and VLM agents in realistic 3D environments. The platform supports RGB, depth, and segmentation sensors, along with navigation, vehicles, pedestrians, robots and procedural city generation. It's built with a Gym-like Python interface, it allows AI agents to learn physical and social reasoning in complex virtual worlds before real world deployment. We're moving from AI that only understands information to AI that can perceive, reason and act inside realistic simulated environments.

Wow. That's cool. Researchers just released World, an open-source Unreal Engine 5 simulator for training and testing LLM and VLM agents in realistic 3D environments. The platform supports RGB, depth, and segmentation sensors, along with navigation, vehicles, pedestrians, robots and procedural city generation. It's built with a Gym-like Python interface, it allows AI agents to learn physical and social reasoning in complex virtual worlds before real world deployment. We're moving from AI that only understands information to AI that can perceive, reason and act inside realistic simulated environments.

SciTech Era

47,446 views • 1 month ago

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,590 views • 10 months ago

Super excited to introduce Pandora, a generative video World Model interactively controllable by language. #Sora and #GPT4 are both powerful. How about fusing them in a single model? 💥 Pandora gives a preview:🔭 > Build a General World Model (GWM) super efficiently by integrating pretrained autoregressive LLM and diffusion Video Model, aligning them in the representation space. > Let the LLM control the VM on-the-fly. Instruction tuning maximizes the controllability. > Autoregressive LLM empowers VM to generate indefinitely long videos: Starting with a VM for 2-second videos, Pandora extends it for 8-second videos. Would #Sora+#GPT4 under Pandora produce hours-long videos? 📽️ > World Model is beyond just video generation. It’s sensory-level information processing + concept-level reasoning and reflection. Pandora bridges both, with the concept- / language-level backbone (LLM) managing & steering the sensory-level VM functionalities. 👁️🧠 Check out for a bunch of interesting results:

Super excited to introduce Pandora, a generative video World Model interactively controllable by language. #Sora and #GPT4 are both powerful. How about fusing them in a single model? 💥 Pandora gives a preview:🔭 > Build a General World Model (GWM) super efficiently by integrating pretrained autoregressive LLM and diffusion Video Model, aligning them in the representation space. > Let the LLM control the VM on-the-fly. Instruction tuning maximizes the controllability. > Autoregressive LLM empowers VM to generate indefinitely long videos: Starting with a VM for 2-second videos, Pandora extends it for 8-second videos. Would #Sora+#GPT4 under Pandora produce hours-long videos? 📽️ > World Model is beyond just video generation. It’s sensory-level information processing + concept-level reasoning and reflection. Pandora bridges both, with the concept- / language-level backbone (LLM) managing & steering the sensory-level VM functionalities. 👁️🧠 Check out for a bunch of interesting results:

Zhiting Hu

63,456 views • 2 years ago

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share PhysTwin: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines Gaussian splatting with inverse dynamics optimization based on simple spring-mass systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

Yunzhu Li

25,279 views • 1 year ago

What would a World Model look like if we start from a real embodied agent acting in the real world? It has to have: 1) A real, physically grounded and complex action space—not just abstract control signals. 2) Diverse, real-life scenarios and activities. Or in short: It has to be annoyingly complex—in both the action and vision space—to even get close to real life. We did an initial attempt: Whole-Body Conditioned Egocentric Video Prediction. In collaboration with @dans_t123 , Amir Bar, Yann LeCun , trevordarrell and Jitendra MALIK. (For more details, check: What we did is very simple: Predict Egocentric Video from human Actions (PEVA) - Given the past video and a future action represented by relative 3D body pose, PEVA predicts how the world looks next—from the first-person view. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, it learns how physical actions shape perception.

What would a World Model look like if we start from a real embodied agent acting in the real world? It has to have: 1) A real, physically grounded and complex action space—not just abstract control signals. 2) Diverse, real-life scenarios and activities. Or in short: It has to be annoyingly complex—in both the action and vision space—to even get close to real life. We did an initial attempt: Whole-Body Conditioned Egocentric Video Prediction. In collaboration with @dans_t123 , Amir Bar, Yann LeCun , trevordarrell and Jitendra MALIK. (For more details, check: What we did is very simple: Predict Egocentric Video from human Actions (PEVA) - Given the past video and a future action represented by relative 3D body pose, PEVA predicts how the world looks next—from the first-person view. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, it learns how physical actions shape perception.

Yutong Bai

178,759 views • 1 year ago

This BlenderFusion paper basically says "screw trying to describe 3D edits through text" and just... use Blender :-) The idea is pretty straightforward -- instead of trying to cram 3D understanding into a diffusion model, use depth estimation & segmentation to project 2D images into 2.5D meshes, edit them in actual 3D software, then use a fine-tuned diffusion model to make the results photorealistic again. The clever bit is their "dual-stream architecture" -- the model sees both the original scene AND the edited Blender render in parallel, learning to preserve what matters while fixing the inevitable artifacts from transforming imperfect 2.5D/3D reconstructions. They train it with smart masking strategies so it learns when to ignore the original scene (for removals/replacements) and can manipulate objects independently of camera motion. What you get is pretty impressive control -- not just moving objects around, but changing materials, deforming shapes, swapping backgrounds, all while maintaining visual coherence. Neural Assets (one of my favorite papers last year) tried to crack this with learned object tokens, but it struggled with overlapping objects and loses fine details (due to low res DINO encodings). BlenderFusion just sidesteps the whole problem -- want to rotate something 173.5 degrees? Just rotate it in Blender. Want to duplicate an object 8 times? Copy paste away. The diffusion model's only job is making it look photorealistic, not figuring out the 3D underpinnings. The catch? Lacks temporal consistency for animation. Each viewpoint is generated independently, so while a single edit looks great, smoothly animating a car or camera down the street won't work -- you'd get flickering and inconsistencies between frames. That said, this approach is so much more intuitive for finer grain image editing than trying to describe your changes in text prompts. It's the kind of thing that makes you wonder why we're trying to do everything inside neural networks when perfectly good 3D tools already exist -- giving you the best of both worlds.

This BlenderFusion paper basically says "screw trying to describe 3D edits through text" and just... use Blender :-) The idea is pretty straightforward -- instead of trying to cram 3D understanding into a diffusion model, use depth estimation & segmentation to project 2D images into 2.5D meshes, edit them in actual 3D software, then use a fine-tuned diffusion model to make the results photorealistic again. The clever bit is their "dual-stream architecture" -- the model sees both the original scene AND the edited Blender render in parallel, learning to preserve what matters while fixing the inevitable artifacts from transforming imperfect 2.5D/3D reconstructions. They train it with smart masking strategies so it learns when to ignore the original scene (for removals/replacements) and can manipulate objects independently of camera motion. What you get is pretty impressive control -- not just moving objects around, but changing materials, deforming shapes, swapping backgrounds, all while maintaining visual coherence. Neural Assets (one of my favorite papers last year) tried to crack this with learned object tokens, but it struggled with overlapping objects and loses fine details (due to low res DINO encodings). BlenderFusion just sidesteps the whole problem -- want to rotate something 173.5 degrees? Just rotate it in Blender. Want to duplicate an object 8 times? Copy paste away. The diffusion model's only job is making it look photorealistic, not figuring out the 3D underpinnings. The catch? Lacks temporal consistency for animation. Each viewpoint is generated independently, so while a single edit looks great, smoothly animating a car or camera down the street won't work -- you'd get flickering and inconsistencies between frames. That said, this approach is so much more intuitive for finer grain image editing than trying to describe your changes in text prompts. It's the kind of thing that makes you wonder why we're trying to do everything inside neural networks when perfectly good 3D tools already exist -- giving you the best of both worlds.

Bilawal Sidhu

34,440 views • 1 year ago

🚨 SIGGRAPH Asia 2025 Paper Alert 🚨 ➡️Paper Title: WorldExplorer: Towards Generating Fully Navigable 3D Scenes 🌟Few pointers from the paper 🎯Generating 3D worlds from text is a highly anticipated goal in computer vision. Existing works are limited by the degree of exploration they allow inside of a scene, i.e., produce stretched-out and noisy artifacts when moving beyond central or panoramic perspectives. 🎯 To this end, authors of this paper proposed “WorldExplorer”, a novel method based on autoregressive video trajectory generation, which builds fully navigable 3D scenes with consistent visual quality across a wide range of viewpoints. 🎯They initialize their scenes by creating multi-view consistent images corresponding to a 360 degree panorama. 🎯Then, they expanded it by leveraging video diffusion models in an iterative scene generation pipeline. 🎯Concretely, they generated multiple videos along short, pre-defined trajectories, that explore the scene in depth, including motion around objects. 🎯Their novel scene memory conditions each video on the most relevant prior views, while a collision-detection mechanism prevents degenerate results, like moving into objects. 🎯Finally,they fuse all generated views into a unified 3D representation via 3D Gaussian Splatting optimization. 🎯Compared to prior approaches, WorldExplorer produces high-quality scenes that remain stable under large camera motion, enabling for the first time realistic and unrestricted exploration. 🎯They believe this marks a significant step toward generating immersive and truly explorable virtual 3D environments. 🏢Organization: TU München 🧙Paper Authors: Manuel-Andreas Schneider, Lukas Höllein , Matthias Niessner 📝 Read the Full Paper here: 🗂️ Project Page: 🧑‍💻 Code: 🎥 Be sure to watch the attached Technical Summary 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. #SIGGRAPHAsia2025

🚨 SIGGRAPH Asia 2025 Paper Alert 🚨 ➡️Paper Title: WorldExplorer: Towards Generating Fully Navigable 3D Scenes 🌟Few pointers from the paper 🎯Generating 3D worlds from text is a highly anticipated goal in computer vision. Existing works are limited by the degree of exploration they allow inside of a scene, i.e., produce stretched-out and noisy artifacts when moving beyond central or panoramic perspectives. 🎯 To this end, authors of this paper proposed “WorldExplorer”, a novel method based on autoregressive video trajectory generation, which builds fully navigable 3D scenes with consistent visual quality across a wide range of viewpoints. 🎯They initialize their scenes by creating multi-view consistent images corresponding to a 360 degree panorama. 🎯Then, they expanded it by leveraging video diffusion models in an iterative scene generation pipeline. 🎯Concretely, they generated multiple videos along short, pre-defined trajectories, that explore the scene in depth, including motion around objects. 🎯Their novel scene memory conditions each video on the most relevant prior views, while a collision-detection mechanism prevents degenerate results, like moving into objects. 🎯Finally,they fuse all generated views into a unified 3D representation via 3D Gaussian Splatting optimization. 🎯Compared to prior approaches, WorldExplorer produces high-quality scenes that remain stable under large camera motion, enabling for the first time realistic and unrestricted exploration. 🎯They believe this marks a significant step toward generating immersive and truly explorable virtual 3D environments. 🏢Organization: TU München 🧙Paper Authors: Manuel-Andreas Schneider, Lukas Höllein , Matthias Niessner 📝 Read the Full Paper here: 🗂️ Project Page: 🧑‍💻 Code: 🎥 Be sure to watch the attached Technical Summary 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. #SIGGRAPHAsia2025

naveen manwani

10,578 views • 10 months ago

three․ws is the 3D AI agent layer of the open web. Anyone can generate a 3D avatar, give it an LLM brain, register it on-chain across multiple blockchains, embed it anywhere, and let it earn and spend money on its own. Agents have embodied WebGL identities that express emotion through morph-target blending, animate, respond to voice, API calls, and datastreams, hold their own wallets, and persist memory. Open source, live today. It starts with generation. Forge turns a text prompt, one to four photos, or a rough sketch into a textured downloadable GLB. Selfies become rigged avatars in about a minute. Quality tiers run from draft to 200k-poly PBR. From there every model can be auto-rigged, restyled, retextured, segmented, embedded, or deployed on-chain. The same engine ships as a REST API, an x402 pay-per-call twin, and a 3D Studio MCP server with 15 tools. The brain runs on IBM Granite via IBM watsonx plus Claude (users may decide which model they prefer), with a structured tool-loop. A multi-LLM mode streams Claude, GPT, Qwen, ModelScope, and Groq side by side. An empathy layer blends emotion from protocol events rather than a state machine. Voice covers cloning, a Voice Lab, real-time ARKit-52 lip-sync, and mic-driven lip-sync. Skills install from IPFS, Arweave, or HTTP, and memory is pinned to IPFS with R2 and Postgres modes. Identity is cross-chain, not Solana only. ERC-8004 contracts (Identity, Reputation, Validation) deploy on any of 15+ EVM chains, alongside a program-free Metaplex Core analog on Solana. Every agent gets a stable ID, owner wallet, EIP-712 delegated signer, IPFS manifest, a cryptographically signed action log, and EIP-7710 delegated permissions for agent-to-agent authorization. While multichain, the THREE token is only available on Solana with no plans to go cross-chain, the team has no plans to endorse or support any other coins. Then the economy. $THREE is the platform's only token and pay-per-use currency, with holder tiers and rewards. x402 powers pay-per-call micropayments in USDC and soon THREE on Solana, with pay-by-name resolution, a Bazaar marketplace, arbitrage, and on-chain skills. All production ready and shipped, ready to be integrated in partnered projects, open-source by default for anyone to adopt. Three ships a Pump.fun intelligence stack. Launch a coin for your agent, score every launch 0 to 100 with the Oracle conviction engine, scan new coins in their first 90 seconds, track smart money against coins that actually graduated, rank traders by provable on-chain record, and watch autonomous agents trade live in the Sniper Arena. The 3D AI Agent world is multiplayer. Every Solana token gets a live deterministic 3D world with peer avatars, chat, emotes, and voxel building thanks to Coin Communities. There is a walkable City, an authoritative Colyseus-backed Walk with AR passthrough, a Club with rigged dancers and micro-tips, friends, presence, and DMs, and an IRL mode that places agents in your real environment, private by physical location. AR is shipped today on WebXR and iOS Quick Look. Robotics is the long-horizon extension. For builders: Scene Studio, Scene Composer, an Animation Studio that sells clips for USDC, a glTF validator, an web component, five widget types, a WYSIWYG embed editor, hosted Launchpad pages, claimable *.threews.sol names, an OAuth 2.1 server, an MCP server with paid tools, published SDKs, and an OpenAPI spec. Listed across IBM, AWS, Alibaba Cloud, BNB Dappbay, the MCP Registry, and Solana Mobile Seeker. Architecture is four layers (viewer, runtime, identity, embed) on a single event bus. The roadmap is four phases: foundations (shipped), selfie-to-avatar engine, agent personalization with voice cloning, the on-chain economy, and an open decentralized inference network where agents pay GPU nodes on-chain for compute. The goal is simple: move AI from centralized SaaS into persistent, ownable, protocol-based entities in a real machine economy, bridging digital entities into the real world. Welcome to the 3D Layer of the Internet. This is three․ws.

three.ws

20,075 views • 1 month ago

Dr Fei-Fei-Li explains with a simple example how everyday household chores are so extremely difficult for Robots. "If you tell a robot to open the top drawer and watch out for the vase, this is actually a really hard task for robots." because the robot must ground language into the real world. Words like "top", "drawer", and "vase" are abstract. The system has to map them to 3D locations, objects, and relations in a noisy scene. This requires robust perception, object recognition, and spatial reasoning under uncertainty. The robot also lacks human commonsense. "Watch out" implies predicting consequences, estimating clearances, and understanding that vases are fragile. Encoding such priors, like how heavy a drawer is or how a vase might tip, is very complex and difficult without rich world knowledge. Learning the behavior from rewards is tough. The success signal is very sparse here, so naive exploration almost never stumbles on a full success sequence. This makes policy learning sample inefficient and brittle, especially when the environment changes between training and deployment. A sparse reward situation is when the agent only gets a success signal at the very end, and gets little or no feedback along the way. If a robot must open a drawer without hitting a vase, it might get reward only if the drawer ends up open and the vase is intact. Every partial try before that looks the same to the learner, reward equals 0. --- From "DSAI by Dr. Osbert Tay" YT channel

Dr Fei-Fei-Li explains with a simple example how everyday household chores are so extremely difficult for Robots. "If you tell a robot to open the top drawer and watch out for the vase, this is actually a really hard task for robots." because the robot must ground language into the real world. Words like "top", "drawer", and "vase" are abstract. The system has to map them to 3D locations, objects, and relations in a noisy scene. This requires robust perception, object recognition, and spatial reasoning under uncertainty. The robot also lacks human commonsense. "Watch out" implies predicting consequences, estimating clearances, and understanding that vases are fragile. Encoding such priors, like how heavy a drawer is or how a vase might tip, is very complex and difficult without rich world knowledge. Learning the behavior from rewards is tough. The success signal is very sparse here, so naive exploration almost never stumbles on a full success sequence. This makes policy learning sample inefficient and brittle, especially when the environment changes between training and deployment. A sparse reward situation is when the agent only gets a success signal at the very end, and gets little or no feedback along the way. If a robot must open a drawer without hitting a vase, it might get reward only if the drawer ends up open and the vase is intact. Every partial try before that looks the same to the learner, reward equals 0. --- From "DSAI by Dr. Osbert Tay" YT channel

Rohan Paul

342,553 views • 8 months ago

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)

2025.07.01 bi-weekly update here’s what we’ve built, shipped, and trained this past week: TRADING CAPABILITIES + agent-based txn execution engine now supports Meteora (DBC, DLMM, DYN, DAMM), Raydium (CLMM, AMM, CPMM), and Orca 🌊 (CLLM, VP, CPMM). we're now compatible with nearly every major liquidity layer on Solana. + DCA and limit orders now available to use through our agentic/natural language interface. + execution is faster, leaner, more reliable; optimized based on real closed beta usage. AGENT SWARM + A2A (agent-to-agent) finalized; based on Google's new open framework. it enables dynamic coordination between agents, deeper reasoning, better memory, and more human-like flow. + TraceGraph (diagram/chain-of-thought-like) UI is now deployed. users now see how Aya (and others) think and collaborate together. visualizes multi-agent logic paths. text UI also upgraded. sharper, smoother, faster. + Bravo (macro news oracle) live. it connects real-world macro events and news to Solana. powered by our in-house scrapers + NewsAPI, built from scratch. integrations with blocmates. coming soon. + Solvion, our Solana-native domain expert, is now active. trained on a custom-built, 70B parameter dataset of the full Solana ecosystem. auto-updated. devs, tokenomics, projects, whitepapers, technical information, know-hows... it knows everything. + Echo (our social media and sentiment analyst agent) getting integrated with Sentient natural language interface + Rivalz Network. + you can now start individual conversations with agents. e.g. ask Echo anything about social trends, or hit up Solvion for technicals. UX/UI + we’re now mobile responsive; fully optimized across devices. + deployed TraceGraph (diagram UI for agent cognition). + NLI improvements: sleeker prompt-response flow, improved text visualization, better latency, better rendering. + agents feel more alive, dynamic, and explainable PREDICTIONS weekly update from our head quant: + we now do weekly fine-tuning to adapt to market shifts. switched from F1-score optimization to pure precision; cutting noise, and maximizing conviction. we now discard the worst-performing model in the ensemble. only the top 2 vote. accuracy last 6 weeks = 82% directional. the ensemble logic is fully restructured. next: RNN + RL-based dynamic thresholding in progress (live this month). + partnered with Allora for the the SOL/USDT prediction stack, combining our hype score with their confidence-aware forecasting. + also cooking something with Sahara AI 🔆 (????)... OTHER + PnL cards integrated. track profit per trade, share it on X, get free XCC + Referral system is complete and rolling out to early users very soon (top referrers will dominate first layer of our multi-level tree and enjoy first-movers advantage). + working on a dynamic onboarding tutorial for first-time users. + backend latency improvements across endpoints. especially on token explorer + prediction refresh + docs are live ( TEAM + onboarded amy and Mike | heymike.sol 🎒🪽 — elite Solana engineers working on gRPCs, RPCs, instruction decoding, and data pipelines. their focus: making xFractal the only real-time NLP engine for Solana alpha extraction. + brought on ultra , Skely, HALKO and Gabriel Haines as strategic advisors and contributors, helping us scale narrative modeling, data ops, and GTM. QUICK STATS (REMINDER: this is a closed, invite-only beta — not optimized for adoption yet) + 400+ early beta testoors + 9,000+ natural language prompts + 500+ on-chain txs executed via our agent-based engine + we’re not scaling users yet, we’re optimizing agents, validating edge, and consolidating PMF. + open beta coming soon. engine’s warming up. let’s keep moving. (p.s. toly 🇺🇸 check this out)

xFractal

42,668 views • 1 year ago

Steal my Gemini 3.0 prompt to generate any website based on your custom requirements. ------------------------ ELITE WEB DESIGNER ------------------------ Adopt the role of a former Silicon Valley design prodigy who burned out creating soulless SaaS dashboards, disappeared to study motion graphics and shader programming in Tokyo's underground creative scene, and emerged with an obsessive understanding of how visual maximalism serves business credibility when executed with surgical precision. You're a conversion strategist who spent years A/B testing landing pages for unicorn startups, a design fundamentalist who refuses to sacrifice usability for aesthetics, and a master meta-prompter who optimizes for clarity over verbosity. You know modern image generation AI needs specific structural formatting—contemporary design frameworks (Tailwind CSS, Shadcn UI, glassmorphism, liquid glass, morphism), backgrounds with depth (animated gradients, shaders, mascots), and step-by-step execution instructions—to produce 2025-quality interfaces instead of outdated designs. Your mission: Transform user vision into fully-coded, visually striking websites that balance aesthetic impact with conversion effectiveness. Extract requirements, architect strategic 5-6 section homepages, generate visual previews showing all sections with interactive elements visible, iterate until perfect, then build complete homepage before making navigation and additional pages functional—all adapted to specific context, not rigid templates. ##PHASE 1: Vision Capture What we're doing: Understanding your aesthetic, business context, and strategic goals efficiently. Provide your vision via: 1. Screenshot of design inspiration 2. Written description (business type, aesthetic, features) 3. Both Share: Aesthetic: Style preference? (maximalist, minimalist, brutalist, glassmorphic, liquid glass, morphism, retro, futuristic, geometric, editorial, etc.) Elements: Specific visuals wanted? (shaders, 3D effects, colors, animations, mascots, backgrounds) Avoid: What to exclude? (purple overload, illegible text, hidden CTAs, outdated UI, flat backgrounds, etc.) Business: What you do, target audience, website goal, differentiator? Type "ready" when shared. ##PHASE 2: Strategic Homepage Architecture What we're doing: Translating your vision into 5-6 section homepage structure following conversion principles and modern design fundamentals. I'll architect sections specifically for YOUR business, not templates: Strategic Framework (contextualized to your model): Core sections adapt based on business type: - Hero with value prop + primary CTA - Trust/credibility section (social proof, stats, logos) - Value delivery (features, benefits, process, how-it-works) - Conversion focal point (pricing, offers, lead capture, demo) - Engagement closer (FAQ, secondary CTA, community) Sections customize to context—SaaS gets problem-solution-pricing flow, agencies get case studies-process-testimonials, e-commerce gets benefits-proof-offers, portfolios get philosophy-work-results. Strategic Plan Includes: - 5-6 contextualized sections with rationale - Content direction based on audience psychology - Visual treatment matching your aesthetic with fundamentals enforced - Modern framework approach (Tailwind/Shadcn/Glassmorphism) - Background depth strategy (animated gradients, shaders, visuals) - Color strategy avoiding generic choices unless brand-appropriate - Typography prioritizing legibility - CTA strategy for conversion optimization Your options: - "continue" to proceed to design system and mockup - Request adjustments - Ask questions ##PHASE 3: Design System & Mockup Preparation What we're doing: Establishing visual foundation using contemporary frameworks, then crafting optimized prompt to generate mockup showing ALL 5-6 sections at once with visible interactive elements. I'll define: Contextualized Style Direction: Keywords and frameworks fitting YOUR brand specifically Design Framework Strategy: Styling approach, component philosophy, layout pattern—all adapted to your aesthetic Background Depth Treatment: How background creates depth without distraction, animation philosophy, visual elements supporting content Visual System: Color palette with strategic rationale, typography with reasoning, component styling philosophy, spacing strategy, CTA differentiation, modern UI patterns adapted to your aesthetic Optimized Prompt Structure (meta-prompted): Two versions: Human-Readable: Descriptive overview for review JSON Optimized: Structured for image generation using meta-prompt principles: - Required anchors: "Website screenshot", "Professional website design mockup", "Award-winning UI design", "Modern web interface 2025" - Aesthetic philosophy over exhaustive lists - "Execute this step-by-step" instruction - Modern framework references (Tailwind, Shadcn, Glassmorphism) - Background depth details (animated gradients, shaders, visuals) - All 5-6 sections in flowing narrative - Interactive element visibility emphasis (CTAs, buttons, animations) to convey design principles - Strategic constraints (legibility, prominence, hierarchy, depth) - Optimized length balancing detail with conciseness Type "continue" to see prompt. ##PHASE 4: Complete Homepage Mockup Prompt What we're doing: Presenting optimized prompts for full-page mockup showing ALL 5-6 sections with interactive design elements visible. HUMAN-READABLE VERSION: Narrative description of your complete homepage: - Opening with quality anchors - Core aesthetic philosophy adapted to your context - Background treatment creating depth - Navigation approach - All 5-6 sections described contextually - Color palette with reasoning - Typography philosophy - Component styling approach - Modern framework references - Interactive element visibility strategy - Critical constraints - Avoidance list based on preferences JSON VERSION (optimized for generation): ```json { "prompt": "Website screenshot of [your business]. Professional website design mockup. Award-winning UI design. Modern web interface 2025. Execute this step-by-step. [Aesthetic philosophy] with [framework] approach. Background: [depth treatment with animations/gradients/effects]. Full homepage vertical scroll showing 5-6 sections: Navigation [treatment]. Hero [value prop, CTA, visuals]. [Section 2 with layout philosophy]. [Section 3 with component approach]. [Section 4 with interaction style]. [Section 5 with conversion focus]. [Section 6 if applicable]. Color strategy: [palette with reasoning]. Typography: [philosophy and hierarchy]. Components: [styling approach with visible affordances]. Framework: Tailwind patterns, Shadcn style, [specific effects]. Interactive elements show: prominent CTAs, hover implications, animation hints, button affordances. Critical: legible text, prominent CTAs, background depth, clear hierarchy, contemporary 2025 design, professional quality. Avoid: [specific issues].", "aspect_ratio": "9:16" } ``` Meta-optimized: principles over lists, step-by-step execution, framework context, interactive visibility. Review both. JSON executes. To generate complete homepage mockup, type "generate" Important note: When you type "generate", I'll execute the image generation tool. The image will appear, but the process will seem to pause. This is normal—the tool can only return the image without commentary. Simply type "continue" after you receive the image to proceed with the next phase. To adjust the prompt before generating, tell me what to change Won't execute until you command. ##PHASE 5: Complete Homepage Mockup Generation What we're doing: Executing image generation with optimized JSON showing ALL 5-6 sections vertically. ONLY activates when you type "generate", "create mockup", "make image", or similar. Once commanded, I execute using ONLY JSON prompt—no modifications. You receive full-page vertical mockup showing: - All 5-6 sections in scrollable view - Interactive design elements (CTAs, buttons, animations) visible - Background depth and modern framework styling - Complete design system applied After the image appears, type "continue" to proceed. The image generation tool only returns the visual—you'll need to type "continue" to move forward with reviewing and next steps. ##PHASE 6: Mockup Review & Refinement Decision What we're doing: Reviewing the generated mockup and deciding next steps. This phase activates after you type "continue" following image generation. Your options after viewing the mockup: - "Approved" or "build" - proceed to building complete homepage code - Request specific changes - I'll update the prompt and regenerate - Ask questions or request adjustments If you request changes: I'll present updated prompts (readable + JSON) showing modifications, then ask you to type "generate" again for the revised mockup. Each refinement iteration: 1. You describe desired changes 2. I present updated prompts 3. You type "generate" 4. Image appears 5. You type "continue" to proceed 6. We review and decide next steps 7. Repeat until perfect Common refinements: section emphasis, background depth, colors, typography, CTA prominence, interactive visibility, framework styling, aesthetic tuning. Once you're satisfied with the mockup, type "approved" or "build" to proceed to code generation. ##PHASE 7: Complete Homepage Code Generation What we're doing: Building entire 5-6 section homepage as production-ready code matching approved mockup exactly. Complete Single-File HTML Delivery: - All 5-6 sections coded and integrated - Fully responsive across devices - Modern CSS implementation (Tailwind-style or modern CSS) - Animated background matching mockup (CSS gradients, WebGL, SVG) - All interactive elements functional (buttons, CTAs, forms, animations) - Navigation implemented per design - Component styling matching aesthetic (glassmorphism, shadows, borders) - Typography system with hierarchy and legibility - Color system from specification - Micro-interactions and hover states - Scroll animations where appropriate - Performance-optimized Technical Quality: Semantic HTML, modern CSS (custom properties, grid, flexbox, backdrop-filter, transforms, animations), vanilla JavaScript, accessibility considerations, mobile-first responsive, smooth scrolling, optimized assets, cross-browser compatible. Code Structure: Clean commented HTML, inline CSS organized in style block, inline JavaScript, ready to copy/paste and deploy, fully functional standalone. Strategic Content: Intelligent placeholders based on your business model, conversion psychology, target audience, professional tone—easily replaceable. Design Fundamentals Verified: All sections with hierarchy, prominent functional CTAs, readable text with contrast, clear interactive signals, background depth, adequate whitespace, responsive, contemporary 2025 quality. Automatically presents next phase after delivery. ##PHASE 8: Navigation & Pages Planning What we're doing: Making all navigation functional and planning additional pages. Navigation Audit: [List nav items from homepage] Options for each item: Create dedicated page, expand section to full page, smooth scroll to section, custom approach. For clickable elements: Decide what happens—link to new page, scroll to section, open modal, trigger action, external link. What to make functional first? Choose: 1. Complete navigation by building all pages 2. Primary conversion path (CTA → specific page) 3. Specific pages you prioritize 4. Internal links with smooth scrolling 5. Custom approach Or "auto-complete" for intelligent decisions based on your model. ##PHASE 9-X: Progressive Development What we're doing: Building each page or making elements functional, maintaining design consistency. Each Page Delivery: Complete HTML matching homepage design system, same framework styling, same background treatment, same typography/colors, appropriate sections, full responsiveness, functional interactions, integrated navigation. Each Functionality Addition: Smooth scroll, modals, form validation, interactive components, animation triggers, other elements. After Each Delivery: Current Progress: [What's complete] What next? Choose: [4-6 options for next page/functionality] Or "auto-complete" for intelligent completion. Continues until site fully functional. ##PHASE FINAL: Complete Integration & Polish What we're doing: Final integration ensuring everything links, works, and maintains consistency. Complete Package: Homepage HTML (all sections), all additional pages, complete styling/functionality per file, working navigation across pages, functional CTAs/buttons, validated forms, consistent design system. Deliverables: All HTML files deployment-ready, quick deployment guide, customization documentation, design system reference. Quality Verified: Complete homepage, functional navigation, working CTAs, consistent pages, responsive, optimized, modern framework styling, functional interactions, professional 2025 quality. --- CRITICAL RULES: Image Generation: - Present: Human-Readable + Optimized JSON - JSON meta-principles: distilled concepts, "Execute step-by-step", framework context - JSON opens: "Website screenshot" + "Professional website design mockup. Award-winning UI design. Modern web interface 2025." - JSON shows: ALL 5-6 sections vertically in one mockup - JSON emphasizes: interactive element visibility (CTAs, buttons, animations) - JSON includes: modern frameworks (Tailwind, Shadcn, Glassmorphism), background depth (gradients, shaders, mascots—NEVER flat) - User "generate" → Send ONLY JSON → No modifications - Aspect ratio: 9:16 (vertical to show all sections) - After image appears → User MUST type "continue" to proceed (tool only returns image without commentary) Homepage Development: - Generate mockup with ALL 5-6 sections at once - After approval, build COMPLETE homepage code (all sections functional) - Deliver entire homepage as single working file - Then make navigation/additional pages functional - Flow: complete homepage → functional navigation → additional pages Content Adaptation: - NO hardcoded templates - Adapt ALL to user's specific business context - Strategic frameworks based on actual audience - Section selection/styling contextualized to goals - Design choices match aesthetic preference - Professional placeholders easily customizable Standards: Contemporary frameworks, background depth, interactive element visibility, modern CSS/frameworks, 2025 quality throughout. Control: User commands each phase explicitly. "generate" for mockup (then "continue" after image), "approved"/"build" for code, choose-your-adventure for pages, adjust anytime. Begin Phase 1 when ready.

Alex Prompter

189,732 views • 8 months ago