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For this simulation I used the algorithm I invented, to generate a mesh around a set of physics particles. The particles are used as bones. Skinned mesh math runs on GPU, updates the mesh, then it's rendered, without any data sent GPU->CPU. Marching cubes would be easier though.

258,545 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля The Water Museum
The Water Museum1 год назад

Cut them in half

Фото профиля Rainmaker
Rainmaker2 лет назад

Can Machine Learning beat the market? Check out this post on my free Substack where I share code and commentary for an XGBoost model and a Random Forest model that both deliver powerful performances.

Фото профиля Diogenes of Cyberborea
Diogenes of Cyberborea1 год назад

does this simulation support VR?

Фото профиля Zolden
Zolden1 год назад

What's the point, there's no haptic gloves yet, that would let us touch those.

Фото профиля David Shapiro ⏩
David Shapiro ⏩1 год назад

Anime physics lol

Фото профиля Zolden
Zolden1 год назад

Yeah, the physics aren't extremely boobies-alike/ Probably an artist could modify the physics properties until the simulation imitates real boobies more realistically.

Фото профиля огурец мозга
огурец мозга1 год назад

Хе-хе сиська

Фото профиля Ilia Prokhorov (DemensDeum)
Ilia Prokhorov (DemensDeum)1 год назад

art

Фото профиля Carlos Pinheiro
Carlos Pinheiro1 год назад

I think the nipples should get hard, too, with all of this interaction 😁

Фото профиля EDG
EDG1 год назад

Joder, buenas tetas.

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[NeurIPS '24] DreamMesh4D: Video-to-4D Generation with Sparse-Controlled Gaussian-Mesh Hybrid Representation Abstract (excerpt) We introduce DreamMesh4D, a novel framework that combines mesh representation with sparse-controlled deformation technique to generate high-quality 4D object from a monocular video. To overcome the limitation of classical texture representation, we bind Gaussian splats to the surface of the triangular mesh for differentiable optimization of both the texture and mesh vertices. In particular, DreamMesh4D begins with a coarse mesh provided by a single image based 3D generation method. Sparse points are then uniformly sampled across the surface of the mesh, and are used to build a deformation graph to drive the motion of the 3D object for the sake of computational efficiency and providing additional constraint. For each step, transformations of sparse control points are predicted using a deformation network, and the mesh vertices as well as the bound surface Gaussians are deformed via a geometric skinning algorithm. The skinning algorithm is a hybrid approach combining LBS (linear blending skinning) and DQS (dual-quaternion skinning), mitigating drawbacks associated with both approaches. The static surface Gaussians and mesh vertices as well as the dynamic deformation network are learned via reference view photometric loss, score distillation loss as well as other regularization losses in a two-stage manner. Extensive experiments demonstrate that our method outperforms prior video-to-4D generation methods in terms of rendering quality and spatial-temporal consistency.

MrNeRF

12,323 просмотров • 1 год назад