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Flexible Techniques for Differentiable Rendering with 3D Gaussians
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Fast, reliable shape reconstruction is an essential ingredient in many computer vision applications. Neural Radiance Fields demonstrated that photorealistic novel view synthesis is within reach, but was gated by performance requirements for fast reconstruction of real scenes and objects. Several recent approaches have built on alternative shape representations, in particular, 3D Gaussians. We develop extensions to these renderers, such as integrating differentiable optical flow, exporting watertight meshes and rendering per-ray normals. Additionally, we show how two of the recent methods are interoperable with each other. These reconstructions are quick, robust, and easily performed on GPU or CPU. For code and visual examples, see https://leonidk.github.io/fmb-plus
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Articulate That Object Part (ATOP): 3D Part Articulation via Text and Motion Personalization
ATOP personalizes a pre-trained multi-view diffusion model with a few reference videos to generate part motion from text and masks, then lifts that motion to a 3D articulation axis via score distillation.
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