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๐Ÿ“ข 3D world models from video diffusion suffer from inconsistent frames -> blurry output. Our fix: instead of naรฏve 3D reconstruction, we non-rigidly align each frame into a globally-consistent 3DGS representation. ->sharp visuals on top of any VDM!

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Matthias Niessner ็š„ๅคดๅƒ
Matthias Niessner7 ไธชๆœˆๅ‰

The core idea of our method is a tailored non-rigid frame-to-model ICP that aligns per-frame pointclouds into a canonical world space. This resolves geometric inconsistencies across frames, which we leverage as initialization for our non-rigid 3D reconstruction. The resulting 3D reconstructions have high visual fidelity without floating artifacts. Our approach is agnostic to many state-of-the-art video models; i.e., we can resolve their inconsistencies and turn them into 3D world generators - check out the results on our project page. Great work by the amazing @LukasHollein

Bohan Zeng ็š„ๅคดๅƒ
Bohan Zeng7 ไธชๆœˆๅ‰

Impressive work! could you please leave a issue in our unified framework: , and we will add you splendid work in our OpenWorldLib

Dmytro Omelian ๐Ÿ‡บ๐Ÿ‡ฆ ็š„ๅคดๅƒ
Dmytro Omelian ๐Ÿ‡บ๐Ÿ‡ฆ7 ไธชๆœˆๅ‰

this feels like the missing glue layer between "pretty good video" and "usable 3d" curious how far 3dgs can stretch before it breaks on long, messy, handheld clips

Hang Chu ็š„ๅคดๅƒ
Hang Chu1 ไธชๆœˆๅ‰

really like the inverse deformation idea. how much does this depend on DA3? in the examples, planar surfaces seem to hold up better than the lamp and sofa in the middle of the room.

puppy ็š„ๅคดๅƒ
puppy7 ไธชๆœˆๅ‰

interesting work!

็›ธๅ…ณ่ง†้ข‘

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization paper page: Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.

AK

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