AdaMaG is a guidance rule for generative models derived from decomposing continuity-equation effects into divergence and score-parallel terms, with a proof that divergence diverges near the manifold and a time-dependent bound that improves realism at no extra cost.
Frame guidance: Training-free guidance for frame-level control in video dif- fusion models
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HVG-3D uses a 3D-aware diffusion architecture with ControlNet to synthesize high-fidelity hand-object interaction videos from 3D control signals, achieving state-of-the-art spatial fidelity and temporal coherence on the TASTE-Rob dataset.
Self-refining video sampling treats a pre-trained generator as a denoising autoencoder for iterative inference-time refinement guided by self-consistency uncertainty to improve motion coherence and physics alignment.
GeCo is a new geometry-based metric that produces dense maps of motion and structure inconsistencies in video generation by fusing residual motion and depth priors.
citing papers explorer
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Probability-Conserving Flow Guidance
AdaMaG is a guidance rule for generative models derived from decomposing continuity-equation effects into divergence and score-parallel terms, with a proof that divergence diverges near the manifold and a time-dependent bound that improves realism at no extra cost.
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HVG-3D: Bridging Real and Simulation Domains for 3D-Conditional Hand-Object Interaction Video Synthesis
HVG-3D uses a 3D-aware diffusion architecture with ControlNet to synthesize high-fidelity hand-object interaction videos from 3D control signals, achieving state-of-the-art spatial fidelity and temporal coherence on the TASTE-Rob dataset.
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Self-Refining Video Sampling
Self-refining video sampling treats a pre-trained generator as a denoising autoencoder for iterative inference-time refinement guided by self-consistency uncertainty to improve motion coherence and physics alignment.
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GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure
GeCo is a new geometry-based metric that produces dense maps of motion and structure inconsistencies in video generation by fusing residual motion and depth priors.