Game-ready Medieval Weapons Pack now available. Clean topology. Optimized. Engine-ready. Perfect for fantasy RPGs and indie devs. Available now at #gamedev# #indiedev# #3Dmodeling# #Unity# #UnrealEngine# #Godot# #RPG# #GameAssets# #IndieGameDev# #3DAssets#

Review 3D assets without screenshots or email chains. Unity Asset Manager for industry gives your team a secure cloud workspace to annotate, compare versions, and track changes in real time, no code required. 🔗 Watch the full tutorial:

Hy 3D WorldClaw is out🤯 An agentic workflow that turns a text prompt into a large scale 3D open world. Every scene is freely explorable🌻, built from editable, game-ready 3D assets with real geometry and textures. actual meshes, not video or splats. Project page 👉

Generate 3D. Build your game. Stay in flow. Tripo 3D is now integrated with Combos, so you can create 3D assets and bring them straight into your game—all in one workflow. From idea to playable, faster. @tripoai

If you're vibe coding games or 3D assets with GPT 6 Astra, by default it'll still output pretty basic, sloppy-looking visuals...unless you use this prompt hack. You need to include a variant of the following in your prompt. Get it to spin up a separate critic agent, which takes screenshots of your generation at different angles and gives it a score from 0 to 10 (define what this means). Then, get it to keep looping until it achieves a score of >8. More details in my upcoming review video.

🧊 Turning one image into 3D used to force a choice: "accurate on the visible surface but no backside" or "complete but misaligned with the input." World Tracing stacks 3D points per pixel into layers, capturing visible and hidden surfaces at once. Title: World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible URL: 🔍 Overview World Tracing represents geometry as an ordered stack of L camera-space 3D points per pixel. Layer 0 is the visible surface, deeper layers record front-to-back intersections with surfaces hidden behind the foreground, unifying faithful reconstruction and generative completion as one layered problem. ❓ Challenges Solved Image-to-3D carried a fundamental trade-off. ・Depth estimators are pixel-accurate but stop at the visible surface ・Generative 3D models are complete but work in canonical frames, so they misalign with the input World Tracing frames this as faithful generation: accurately reconstruct the visible surface while plausibly generating the invisible. 💡 Methodology & Proposed Approach At its core is WT-DiT, a 1.7B-parameter diffusion transformer. ・Three-way factorized attention (layer-wise, ray-wise, global) preserves depth ordering and front-to-back coherence ・A mixed noise schedule handles the asymmetry between layer 0 (image-constrained, reconstruction-like) and deeper, generative layers by varying noise per layer ・Mix-training lets multilayer (3D assets) and single-layer (RGBD photos) supervision train together 🎯 Use Cases ・Text-driven 3D scene editing (training-free closed-form compositing thanks to pixel alignment) ・Geometry-conditioned novel-view video synthesis using complete hidden geometry as memory ・A TRELLIS hybrid that yields faithful meshes which reproject correctly to the input 📊 Experimental Results It outperforms prior work on object, scene, and dynamic benchmarks. ・Object visible-depth MAE 0.0149 (VGGT 0.0257) ・Complete-shape F-score@0.05 0.549 (TRELLIS 0.204) ・Scene MAE 0.0102, and best dynamic-clip Chamfer L2 at 0.0105 #3DGeneration# #ComputerVision#

Coding, 3D creation, and financial research with open weights. Kimi K3.1 Pro is Moonshot AI's multimodal MoE with 2.8T total parameters, 104B active, and a 1M-token context. 🤖 💻 Stronger coding execution: Scores 65.7 on Terminal-Bench 4.0. On the internal Kimi Code Bench 3.0 Long-horizon, it improves from K3's 55.95 to 61.2. 🔎 Research with tools: Reaches 70.0 on HLE-Full with tools and 92.1 on BrowseComp in the published evaluations. 🧊 Build and refine 3D assets: Writes and runs Blender and CAD scripts, scoring 92.7 Voxel IoU on BenchCAD with tools. 🏆 Financial document analysis: Achieves SOTA on OfficeQA Pro v2 at 64.4, covering retrieval, cross-document verification, and multi-step numerical reasoning. ✅ Fewer unsupported claims: Claim-level hallucinations fall from K3's 23.4% to 8.3% on the internal Kimi Hallu Bench Hard with tools, a stress test rather than a measure of everyday error rates. 📜 Kimi K3.1-Pro License; commercial-use and attribution conditions apply.

Hi3D V1.0 (formerly Sparc3D) is building on last year's 3D research with roughly 2 million training assets, up from 500,000, according to the team. Hi3D (@Hi3D_AI) has now released the code and weights of V1.0, a model the team says has already been validated in real production workflows. In the team's technical report, V1.0 leads the compared models on multi-view fidelity and detail richness, with a 90% slicing success rate. For anyone building 3D assets, the promise here is preserving the small parts and unseen sides that a front-view render can hide. Release (try it free online now, no deployment needed): GitHub: