D2PO learns better low-NFE diffusion timestep schedules and CFG weights via DPO on a score-based energy with a dynamic denser-schedule preference target.
In: ICML (2015)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
HO-Flow synthesizes realistic hand-object motions from text and canonical 3D objects via an interaction-aware VAE and masked flow matching, reporting SOTA physical plausibility and diversity on GRAB, OakInk, and DexYCB.
citing papers explorer
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D2PO: Optimizing Diffusion Samplers via Dynamic Preference
D2PO learns better low-NFE diffusion timestep schedules and CFG weights via DPO on a score-based energy with a dynamic denser-schedule preference target.
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HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching
HO-Flow synthesizes realistic hand-object motions from text and canonical 3D objects via an interaction-aware VAE and masked flow matching, reporting SOTA physical plausibility and diversity on GRAB, OakInk, and DexYCB.