Zang et al., "World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible"
A Diffusion Transformer that estimates multiple layers of depth to further estimate occluded parts as well.
metricscenes.github.io
Zeng et al., “i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models”
A fully reproducible recipe & code & weights and everything for a truly open text-to-image model. A LOT of interesting findings.
Xiangli et al., "Honey, I Shrunk the Arc de Triomphe!"
Metric depth estimators aren't actually metric. With curated, scaled data, they can be adapted to be better.
2dlfm.github.io
Esmati and Nath et al., "The Invisible Hand of Physics: When Video Diffusion Models Know More Than They Show"
You can use inversion to retrieve feature representations for a video, which can be linearly decoded into physical plausibility -- if you use enough steps not shortcuts
Dabhi and Gill et al., "2D-LFM: Lifting Foundation Model without 3D Supervision"
Simply using transformers to do 2D-to-3D lifting of 2D landmarks fails by construction due to the permutation equivariance of the architecture -- inject positional encoding in multiple layers to fix
Modern video diffusion models generate increasingly realistic and temporally coherent videos, motivating their use as candidate world simulators. Yet it remains unclear whether these models internally...
