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📢WorldMesh is accepted to #ECCV2026, and we're releasing the code today! 🎉 Led by mschneider456: navigable, multi-room 3D scenes from a text prompt, with a mesh scaffold conditioning image diffusion for global consistency + photorealistic detail 👇

42,980 次观看 • 3 个月前 •via X (Twitter)

8 条评论

Angela Dai 的头像
Angela Dai3 个月前

Key idea: decouple structure from appearance. @mschneider456 builds a mesh scaffold (walls, floors, objects) from the prompt, then renders it to condition image synthesis, anchoring realistic appearance to explicit 3D geometry that stays consistent across rooms and close-ups

Angela Dai 的头像
Angela Dai3 个月前

Synthesized views are fused into 3D Gaussian splats, giving a navigable world with multi-room connectivity and robust 3D consistency. It scales linearly with the number of rooms.

clankr 的头像
clankr3 个月前

@mschneider456 A very clean implantation. Frankly, there seems to be a very significant leap in realism, which definitely has many applications in both model training and robotic simulation.

ValentynShum 的头像
ValentynShum3 个月前

@mschneider456 Wow

Emircan ERKUL 的头像
Emircan ERKUL3 个月前

@mschneider456 Cool idea! simplicity again wins! What left is extream light advanced AR glasses. RIP another 100 sector/product.

晚晚 的头像
晚晚3 个月前

@mschneider456 这个多房间一致性看着挺难搞,代码开源太及时了

BlakeTheCoder 的头像
BlakeTheCoder3 个月前

@mschneider456 Really great work. I love the decoupling here. Structure before appearance finally solves the consistency problem end-to-end methods ignore.

Daniel 的头像
Daniel3 个月前

@MattNiessner @mschneider456 This is really cool!

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Mnimiy

106,339 次观看 • 1 天前

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,530 次观看 • 3 年前