pith. sign in

Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

fields

cs.CV 3

years

2026 2 2025 1

verdicts

UNVERDICTED 3

representative citing papers

MultiAnimate: Pose-Guided Image Animation Made Extensible

cs.CV · 2026-02-25 · unverdicted · novelty 7.0

MultiAnimate adds Identifier Assigner and Identifier Adapter modules to diffusion video models so they can handle multiple characters without identity mix-ups, generalizing from two-character training data to more characters.

PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion

cs.CV · 2025-11-24 · unverdicted · novelty 7.0

PartDiffuser is a semi-autoregressive discrete diffusion framework that generates high-fidelity 3D meshes from point clouds by combining inter-part autoregression with intra-part parallel diffusion using a part-aware DiT architecture.

PureCC: Pure Learning for Text-to-Image Concept Customization

cs.CV · 2026-03-08 · unverdicted · novelty 5.0

PureCC introduces a decoupled learning objective, dual-branch training pipeline with frozen extractor, and adaptive guidance scale λ* for high-fidelity concept customization while preserving original model behavior in text-to-image generation.

citing papers explorer

Showing 3 of 3 citing papers.

  • MultiAnimate: Pose-Guided Image Animation Made Extensible cs.CV · 2026-02-25 · unverdicted · none · ref 6

    MultiAnimate adds Identifier Assigner and Identifier Adapter modules to diffusion video models so they can handle multiple characters without identity mix-ups, generalizing from two-character training data to more characters.

  • PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion cs.CV · 2025-11-24 · unverdicted · none · ref 9

    PartDiffuser is a semi-autoregressive discrete diffusion framework that generates high-fidelity 3D meshes from point clouds by combining inter-part autoregression with intra-part parallel diffusion using a part-aware DiT architecture.

  • PureCC: Pure Learning for Text-to-Image Concept Customization cs.CV · 2026-03-08 · unverdicted · none · ref 8

    PureCC introduces a decoupled learning objective, dual-branch training pipeline with frozen extractor, and adaptive guidance scale λ* for high-fidelity concept customization while preserving original model behavior in text-to-image generation.