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interpolating image embeddings across arbitrary numbers of vertices. using polygons as lenses through n-dimensional latent space.

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harley turan

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23,143 просмотров • 1 год назад •via X (Twitter)

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exploring paths through latent space by interpolating between known embeddings
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Pitching has been outstanding across Kansas baseball this spring. ⚾️ Every class has difference-makers on the mound right now, power arms, competitors in big moments and guys putting together dominant seasons as postseason play gets closer. Here’s a look at some pitchers with great numbers to watch across Kansas baseball from 2-1A through 6A. Who else joins the conversation? Stats as of May 5th. #sportsinkansas

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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.

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