World Tracing introduces a multi-layer pixel-aligned 3D point representation instantiated via a diffusion transformer (WT-DiT) trained with pixel-space flow matching to jointly reconstruct visible surfaces and generate occluded geometry.
Ldm3d: Latent diffusion model for 3d, 2023
3 Pith papers cite this work. Polarity classification is still indexing.
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UniGP unifies controllable generation and dense prediction in an MMDiT-based diffusion model through simple joint training that preserves backbone priors.
Modality Forcing lets a single DiT produce image and depth outputs in any order after training on sparse real-world depth, with larger image-pretrained models yielding better depth accuracy and a 57% AbsRel reduction versus prior joint generative baselines.
citing papers explorer
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World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible
World Tracing introduces a multi-layer pixel-aligned 3D point representation instantiated via a diffusion transformer (WT-DiT) trained with pixel-space flow matching to jointly reconstruct visible surfaces and generate occluded geometry.
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UniGP: Taming Diffusion Transformer for Prior-Preserved Unified Generation and Perception
UniGP unifies controllable generation and dense prediction in an MMDiT-based diffusion model through simple joint training that preserves backbone priors.
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Modality Forcing for Scalable Spatial Generation
Modality Forcing lets a single DiT produce image and depth outputs in any order after training on sparse real-world depth, with larger image-pretrained models yielding better depth accuracy and a 57% AbsRel reduction versus prior joint generative baselines.