THOM is a training-free two-stage framework that generates physically plausible hand-object 3D meshes directly from text by combining text-guided Gaussians with contact-aware physics optimization and VLM refinement.
Latent-nerf for shape-guided generation of 3d shapes and textures
2 Pith papers cite this work. Polarity classification is still indexing.
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Splatent recovers fine details for latent-space 3D Gaussian Splatting by applying multi-view attention in 2D rather than reconstructing in 3D space.
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THOM: Generating Physically Plausible Hand-Object Meshes From Text
THOM is a training-free two-stage framework that generates physically plausible hand-object 3D meshes directly from text by combining text-guided Gaussians with contact-aware physics optimization and VLM refinement.
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Splatent: Splatting Diffusion Latents for Novel View Synthesis
Splatent recovers fine details for latent-space 3D Gaussian Splatting by applying multi-view attention in 2D rather than reconstructing in 3D space.