Allo{SR}^2 rectifies one-step super-resolution trajectories with allomorphic generative flows via SNR initialization, velocity supervision, and self-adversarial matching to deliver state-of-the-art fidelity and realism.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
4 Pith papers cite this work. Polarity classification is still indexing.
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DiffHDR converts LDR videos to HDR by formulating the task as generative radiance inpainting in a video diffusion model's latent space, using Log-Gamma encoding and synthesized training data to achieve better fidelity and stability than prior methods.
Latent diffusability is quantified by decomposing the MMSE rate along diffusion trajectories into Fisher Information and Fisher Information Rate, with three geometric penalties (dimensional compression, tangential distortion, curvature injection) identified as sources of failure.
DreamEdit3D learns separate token embeddings for segmented object components via two-phase multi-view optimization to enable text-guided 3D editing with consistent image generation and mesh reconstruction.
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Understanding Latent Diffusability via Fisher Geometry
Latent diffusability is quantified by decomposing the MMSE rate along diffusion trajectories into Fisher Information and Fisher Information Rate, with three geometric penalties (dimensional compression, tangential distortion, curvature injection) identified as sources of failure.