SurGe improves local surface geometry in feedforward point maps via gradient matching loss and Neighborhood Attention Decoder, topping average rank on eight zero-shot monocular geometry benchmarks for global AbsRel while boosting local metrics.
Pixel-perfect depth with semantics-prompted diffusion transformers.arXiv preprint arXiv:2510.07316, 2025a
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GemDepth adds explicit camera-pose geometry embeddings and an alternating spatio-temporal transformer to produce sharper, more temporally consistent video depth maps than prior smoothing-based methods.
M²-REPA decouples modality-specific features inside a diffusion model and aligns each to its matching expert foundation model via an alignment loss plus a decoupling regularizer, yielding better visual quality and long-term consistency in multi-modal video generation.
VolFill uses a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into latent space and a latent Diffusion Transformer to denoise complete scenes, conditioned on geometry foundation models, outperforming baselines on SCRREAM and NRGB-D datasets.
WildPose unifies feedforward 3D features from MASt3R with differentiable bundle adjustment for robust monocular pose estimation across dynamic, static, and low-ego-motion scenes.
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Divide and Conquer: Decoupled Representation Alignment for Multimodal World Models
M²-REPA decouples modality-specific features inside a diffusion model and aligns each to its matching expert foundation model via an alignment loss plus a decoupling regularizer, yielding better visual quality and long-term consistency in multi-modal video generation.