AnyBokeh performs any-to-any bokeh editing by estimating source blur via signed circle-of-confusion and disparity maps, deriving an optical fingerprint from their linear relation, and using a generative model conditioned on source and target maps.
Scalable diffusion models with transformers
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Germline-absorbing discrete diffusion uses the germline sequence as the absorbing state to reduce germline bias in antibody modeling, raising non-germline residue prediction accuracy from 26% to 46% and improving conditional generation tradeoffs over EvoProtGrad.
Bernini is a framework that uses an MLLM planner to output semantic representations for a DiT renderer to generate or edit videos, reporting SOTA benchmark performance.
STARRY uses unified diffusion to align spatial-temporal world predictions with action generation plus GASAM for geometry-aware attention, reaching 93.82%/93.30% success on 50 bimanual tasks in simulation and raising real-world success from 42.5% to 70.8%.
citing papers explorer
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AnyBokeh: Physics-Guided Any-to-Any Bokeh Editing with Optical Fingerprint Transfer
AnyBokeh performs any-to-any bokeh editing by estimating source blur via signed circle-of-confusion and disparity maps, deriving an optical fingerprint from their linear relation, and using a generative model conditioned on source and target maps.
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Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion
Germline-absorbing discrete diffusion uses the germline sequence as the absorbing state to reduce germline bias in antibody modeling, raising non-germline residue prediction accuracy from 26% to 46% and improving conditional generation tradeoffs over EvoProtGrad.
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Bernini: Latent Semantic Planning for Video Diffusion
Bernini is a framework that uses an MLLM planner to output semantic representations for a DiT renderer to generate or edit videos, reporting SOTA benchmark performance.
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STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation
STARRY uses unified diffusion to align spatial-temporal world predictions with action generation plus GASAM for geometry-aware attention, reaching 93.82%/93.30% success on 50 bimanual tasks in simulation and raising real-world success from 42.5% to 70.8%.