The paper introduces a DiT-based flow-matching model that generates linear images by synthesizing text-conditioned exposure brackets to preserve full dynamic range.
Denoising diffu- sion probabilistic models.NeurIPS
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.CV 5verdicts
UNVERDICTED 5representative citing papers
A coarse-to-fine pipeline deforms 3D meshes to reflect geometric features from an image using diffusion model representations while preserving topology and part-level semantics.
Noise optimization during sampling recovers diversity in mode-collapsed diffusion models while preserving output fidelity.
EvObj learns evolving object-centric representations for unsupervised 3D instance segmentation by dynamically refining object candidates and completing partial geometries to bridge the synthetic-to-real domain gap, outperforming baselines on real and synthetic datasets.
PNG model learns high-dimensional prompt features to generate realistic noisy sRGB images consistent with input noise distribution without camera metadata.
citing papers explorer
-
Linear Image Generation by Synthesizing Exposure Brackets
The paper introduces a DiT-based flow-matching model that generates linear images by synthesizing text-conditioned exposure brackets to preserve full dynamic range.
-
Image-Guided Geometric Stylization of 3D Meshes
A coarse-to-fine pipeline deforms 3D meshes to reflect geometric features from an image using diffusion model representations while preserving topology and part-level semantics.
-
It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models
Noise optimization during sampling recovers diversity in mode-collapsed diffusion models while preserving output fidelity.
-
EvObj: Learning Evolving Object-centric Representations for 3D Instance Segmentation without Scene Supervision
EvObj learns evolving object-centric representations for unsupervised 3D instance segmentation by dynamically refining object candidates and completing partial geometries to bridge the synthetic-to-real domain gap, outperforming baselines on real and synthetic datasets.
-
Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning
PNG model learns high-dimensional prompt features to generate realistic noisy sRGB images consistent with input noise distribution without camera metadata.