TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
Srinivasan, Matthew Tancik, Jonathan T
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WildSplatter jointly learns 3D Gaussians and appearance embeddings from unconstrained photo collections to enable fast feed-forward reconstruction and flexible lighting control in 3D Gaussian Splatting.
GenRe distills generative priors from diffusion models across scenes to enhance pretrained 3D Gaussian representations for reliable rendering at unseen viewpoints in urban reconstruction.
SCOUP decouples 2D sparse code learning from 3D Gaussian optimization to deliver up to 400x training speedup and 3x better memory efficiency while matching accuracy on open-vocabulary 3D queries.
Probability-Flow Distillation exactly matches the Wasserstein gradient flow of the target distribution when distilling 2D diffusion priors into 3D models, yielding higher-fidelity results than SDS or SDI.
FactorizedHMR recovers 3D human meshes from video by deterministically anchoring the torso-root then probabilistically completing distal articulations via flow-matching with geometry-aware supervision and a synthetic data pipeline.
citing papers explorer
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TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
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WildSplatter: Feed-forward 3D Gaussian Splatting with Appearance Control from Unconstrained Images
WildSplatter jointly learns 3D Gaussians and appearance embeddings from unconstrained photo collections to enable fast feed-forward reconstruction and flexible lighting control in 3D Gaussian Splatting.
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Diffusion-guided Generalizable Enhancer for Urban Scene Reconstruction
GenRe distills generative priors from diffusion models across scenes to enhance pretrained 3D Gaussian representations for reliable rendering at unseen viewpoints in urban reconstruction.
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Sparse Code Uplifting for Efficient 3D Language Gaussian Splatting
SCOUP decouples 2D sparse code learning from 3D Gaussian optimization to deliver up to 400x training speedup and 3x better memory efficiency while matching accuracy on open-vocabulary 3D queries.
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Probability-Flow Distillation: Exact Wasserstein Gradient Flow for High-Fidelity 3D Generation
Probability-Flow Distillation exactly matches the Wasserstein gradient flow of the target distribution when distilling 2D diffusion priors into 3D models, yielding higher-fidelity results than SDS or SDI.
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FactorizedHMR: A Hybrid Framework for Video Human Mesh Recovery
FactorizedHMR recovers 3D human meshes from video by deterministically anchoring the torso-root then probabilistically completing distal articulations via flow-matching with geometry-aware supervision and a synthetic data pipeline.