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Working on sparse volumetric light maps for WebGPU. Got generation part sorted. The resolution is adaptive, refining near geometry and skipping through most of the empty space. The video is question uses a map that's only 10MB in size, including everything. That's less the amount of GPU memory that...

18,651 Aufrufe • vor 4 Monaten •via X (Twitter)

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Working on Sparse Volumetric Light-maps. Thanks to CynicatPro🎃 for pointing me at Unreal's version. In a nutshell it's just another sparse voxel data structure. My implementation is, no doubt, different from Epic Games Store's own. I'm using 4x4x4 probe grid with intermediate nodes having very wide branching factor of 64 as well (4x4x4). I liked the parameters that Unreal is using, of limiting both total memory as well as the lowest level of detail, which is common in sparse grid implementations. Here's Bistro scene with just 1Mb limit. This is roughly equivalent to a 512x512 lightmap texture in 2d, except surface light maps require unique UVs and you typically get very little detail out of 512 resolution texture with a lot of light leaking. There is also no directional response. My implementation encodes second-order spherical harmonics for each probe (9 coefficients), encoding RGB channels as RGBE9995 (4 bytes). So far only worked on the structure, actual bake is yet to come. I've been eyeing sparse voxel structures for a while now, and have been studying them roughly since the GigaVoxel paper by Cyril Crassin but never really implemented anything for the GPU before. I was always the BVH-kind of guy. It's a fascinating topic. --- Stats for the scene: --- Total memory usage: 1.000 MB Node count: 609 Unique probe count: 24,025 Probe reuse: 38.36 % Unexpanded nodes: 15,714 --- Again, note that there is no GI going on here, only the structure of the probe tree and the algorithm for building it from a given scene.

Alex Goldring

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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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