Proposes discretized Matérn process noise for triangulation-agnostic flow matching on meshes with PoissonNet denoiser, tested on elastic states and humanoid poses for meshes exceeding one million triangles.
Meshdiffusion: Score-based generative 3d mesh model- ing.arXiv preprint arXiv:2303.08133,
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DiffuMeta uses diffusion transformers and algebraic language representations to generate diverse 3D shell metamaterials with targeted stress-strain responses under large deformations including buckling and contact.
Art3D enhances flat-colored 2D illustrations with 3D illusion using pre-trained 2D model features and VLM realism evaluation, then generates 3D, while introducing the Flat-2D benchmark dataset.
BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than standard SDS.
MVDream is a multi-view diffusion model that functions as a generalizable 3D prior, enabling more consistent text-to-3D generation and few-shot 3D concept learning from 2D examples.
citing papers explorer
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Mat\'ern Noise for Triangulation-Agnostic Flow Matching on Meshes
Proposes discretized Matérn process noise for triangulation-agnostic flow matching on meshes with PoissonNet denoiser, tested on elastic states and humanoid poses for meshes exceeding one million triangles.
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Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers
DiffuMeta uses diffusion transformers and algebraic language representations to generate diverse 3D shell metamaterials with targeted stress-strain responses under large deformations including buckling and contact.
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Art3D: Training-Free 3D Generation from Flat-Colored Illustration
Art3D enhances flat-colored 2D illustrations with 3D illusion using pre-trained 2D model features and VLM realism evaluation, then generates 3D, while introducing the Flat-2D benchmark dataset.
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BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
BoostDream refines coarse feed-forward text-to-3D assets via 3D distillation, multi-view SDS loss from a 2D diffusion model, and prompt-consistent normal maps to produce higher-quality results more efficiently than standard SDS.
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MVDream: Multi-view Diffusion for 3D Generation
MVDream is a multi-view diffusion model that functions as a generalizable 3D prior, enabling more consistent text-to-3D generation and few-shot 3D concept learning from 2D examples.