WIP 2 Did the first Lighting pass today +... two more scenes! <3 Model by the wonderful 🔞JizzyFox_AfterDark🔞 #fnaf #fnafNSFW #fnafanimation #blender #yiff #knot #funtimefreddy #blendershow more

Don't bother Lefty MODELS: Mangle by ThineCrawler -Original model... from Disembowell - Lefty by 🔞JizzyFox_AfterDark🔞 HIGH QUALITY: #Blender #3D #Animation #FNAFshow more

Oh lawd he comin' 👀 Why is it always... the lil guys that are so much fun to animate? <3 Model by F://Willis #animation #3dart #blender #gamedev #indiedevshow more

Tested Claude + Blender today with two real workflows.... Sharing what actually happened. First one was a simple demo. Gave it one sentence to build a cyberpunk room. Geometry, lighting, camera, render. It got there, though not everything in the prompt landed perfectly. Good enough to show the concept, not a stress test. Second one was more interesting. Threw a raw photogrammetry scan at it. Massey Ferguson tractor, 250k faces, grass background everywhere, debris floating all over the place. Asked Claude to clean it up autonomously. It did the work. Removed 106 floating chunks, cut face count by 87%, stripped the grass, flipped the model upright, set up studio lighting and rendered. Technically it worked. But honest answer: the result wasn't clean enough for production. This kind of cleanup still needs the right tools and someone who knows what they're doing. So here's where it actually fits. Not the precise technical stuff. The setup work. The repetitive stuff. The things that take forever if you don't already know Blender. It won't replace your 3D artist. It replaces the boring part before they start.show more

The world’s first reasoning video model — powered by... Sora 2, Runway & Veo 3. Create seamless 8K videos up to 60s with studio-grade HDR and hybrid AI precision. Faster, smarter, and more consistent — meet the future of video.show more

The world’s first reasoning video model — powered by... Sora 2, Runway & Veo 3. Create seamless 8K videos up to 60s with studio-grade HDR and hybrid AI precision. Faster, smarter, and more consistent — meet the future of video.show more

The world’s first reasoning video model — powered by... Sora 2, Runway & Veo 3. Create seamless 8K videos up to 60s with studio-grade HDR and hybrid AI precision. Faster, smarter, and more consistent — meet the future of video.show more

This will actually be based on a Fan Visual... Novel Project called "two steps from home", Make sure to check it out cause a lot of effort has been put into it and it looks very promising! I'll more about it in the below! Model by The Great ReLLeRtR🔞 #Signalisshow more

🚨NEW BROKEN PASS🚨 This switch of play is insanely... OP🤣Creates so many goal opportunities and very easy to do too! 1) When you’ve got the ball with your LB/RB call your winger short by pressing R1/RB and pass it to them (Can also player lock this like I did in the clip) 2) Do a R1 square pass (RB X on Xbox) with full power first time and try and aim the pass in behind the opponents fullback. Your wingers are available for this pass 99% of the time , especially in the 4213. 3) Go and score!💪show more

Dreamina Seedance 2.5 is almost here!🔥— today — while... US rollout begins August 7. Transitioning from "prompt-based guessing" to **"reference-driven creation"** — this provides the exact stellar upgrade creators have been waiting for. ✅ All of the Seedance family (2.5, 2.0, and future releases) operates at its lowest-ever tier ✅ Dreamina proves to be not just the perfect platform for Seedance 2.5 — it remains the most affordable way to run it, full-capacity, skipping any add-ons What makes it so remarkable? First video model to accept 50 multimodal references — drag in characters, scenes, reference shots, and tunes all in one go. Eliminate the nightmare of endless prompt writing. 🎬 30-second native clips — unfold complete stories, instead of mere fragments. You get 2 extensions ready in post-editing, plus a robust ultra-long mode extending up to 3 minutes. ✂️ Professional editing— powerful 3D white model preview, integrated green screen workflow, alongside Blender/Maya plugin support. 🌏 10+ languages — making sure lip-sync and subtitles align perfectly. It means zero translation tools needed. Dreamina Seedance 2.5 goes live globally first on Dreamina — undoubtedly the 2026 premier AI video model for creators. Coming soon. Create alongside Seedance 2.0 today and master the ultimate workflow! #dreamina #dreaminapartner #seedance #dreaminaseedance25show more

World Model is trending— let's revisit our HunyuanWorld journey.... We’ve been pioneering open-source 3D world generation in the past two months, and this ride’s only getting started. 🌍 📅 July: HunyuanWorld 1.0 📌 First open-source 3D world model compatible with CG pipelines (Unity/Unreal/Blender) 📌 Hit 2K+ GitHub stars in just two months ⭐—thank you for the love! 📅 August: 1.0-Lite 📌Same top-tier quality, running on consumer GPUs! 📅 September: 1.0-Voyager 📌 Direct 3D output + world memory—taking exploration further! Seamlessly integrated into CG pipelines with layered 3D modeling (assets, terrain, skybox) and fully open-sourced.. we’re fully committed to building open-source spatial intelligence for all! 🚀 💡 Why it matters? ✅ Seamless CG Pipeline Integration: Export generated 3D scenes as standard mesh formats, effortlessly integrating into industry-standard tools like Blender, Unity, and Unreal Engine for direct editing, animation, and physical simulation. ✅ Hierarchical Scene Editing: Deconstruct scenes into semantic layers (sky, background, foreground objects) via instance recognition and layer decomposition, allowing for atomic-level control—independently modify, relocate, or replace objects without rebuilding the entire world. Project page: Github: Amazing creations by Stijn Spanhove camenduru GENEL | AIを用いた動画制作 apolinario 🌐 とりにく Directive Creator 🪥 👇 #AI #3DGeneration #OpenSource #WorldModels #Hunyuan3D #HunyuanWorldshow more

Wan 3.0 is live on Magnific. One generation now... takes 20 references at once: 10 images, 5 audio, 5 videos. Not one style ref. Twenty. What that actually changes: Length : 2 to 30 seconds in a single pass. No stitching, no extending clip by clip and praying the character survives the cut. Native audio : voice, ambience and SFX generated with the video, not laid on top afterwards. Multiple ratios available , and adaptive the model composes for the frame instead of cropping into it. 10 image refs : a character, a product, a location and a lighting look can all hold in the same shot. ⚡ 40% off until September 30. More details in comments 👇 What's the first thing you'd throw 20 references at?show more

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN... SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.show more

THE DEPTH MAP TRICK THAT FIXED DANCE ACCURACY IN... SEEDANCE 2.0 Feed the model a video of someone dancing and it tries to interpret everything- the person, the clothes, the lighting, the room, and somewhere in there, the movement. Feed it a depth map and there's nothing left to interpret but the motion. Most creators trying to transfer a dance to a character reference the source footage directly, then wonder why the choreography drifts. The problem isn't the model - it's that you handed it ten variables when you only wanted one. Here's the workflow 1. Lock the character reference in GPT Image 2 first -face, build, costume, so identity holds independently of whatever motion gets applied to it 2. Convert the source dance footage into a depth map instead of using the raw video -this strips out the original performer's appearance, clothing, and environment entirely 3. Feed the depth map as the motion reference and the character sheet as the identity reference- two separate inputs doing two separate jobs, not one input trying to do both 5. Let the depth map carry only spatial movement -the model receives body position and momentum with no competing information about who's moving or what they look like 6. Keep the character and motion inputs isolated throughout - the moment you mix appearance data into the motion reference, the model starts negotiating between two identities Why this works • Raw footage passes the model everything at once- performer, wardrobe, room, lighting -and the choreography competes with all of it for attention • A depth map is pure spatial information, so the only thing left to transfer is movement • Separating identity from motion means the character can stay locked while the dance stays accurate - normally you're trading one for the other • The accuracy gain isn't the model getting better, it's the model getting fewer decisions to make Use cases: ⁃ Dance and choreography transfer onto original characters ⁃ Motion capture-style workflows without motion capture ⁃ Any sequence where a specific movement needs to survive intact ⁃ Character showcase content built on existing performance footage The character sheet answers who's dancing. The depth map answers how - and keeping those two questions separate is the whole trick.show more

Win one of TEN $500 gift cards💰🍪 What year... did we open our first location in Charlotte??? Comment with your guess to win. It's our little way to say thanks for the warm welcome the city of Charlotte has given us 💙 YES-you can Google the answer. Put your answer in the comments and then run to our other social accounts for more chances to win! We'll pick two winners per social channel. GO GO GO 🏃‍♂️ Two (2) winners will be selected at random and contacted via DM by 10/2. By entering you acknowledge that this giveaway is in no way sponsored, endorsed, administered by or associated with X, and you release X of all responsibility. #TiffsTreats #Giveaway #Charlotteshow more