most citedCLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

31 citations · 32 across the 4 of their papers we have counts for

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cs.CV2025

DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation

Jiawei Liu, Junqiao Li, Jiangfan Deng +11

The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real…

cs.CV2025

Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset

Zhuowei Chen, Bingchuan Li, Tianxiang Ma +8

Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructi…

cs.CV20251 cited

DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning

Fulong Ye, Miao Hua, Pengze Zhang +5

In this paper, we introduce DreamID, a diffusion-based face swapping model that achieves high levels of ID similarity, attribute preservation, image fidelity, and fast inference sp…

cs.CV2025

DreamO: A Unified Framework for Image Customization

Chong Mou, Yanze Wu, Wenxu Wu +15

Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative mode…

cs.CV2025

Phantom: Subject-consistent video generation via cross-modal alignment

Lijie Liu, Tianxiang Ma, Bingchuan Li +6

The continuous development of foundational models for video generation is evolving into various applications, with subject-consistent video generation still in the exploratory stag…

cs.CV202231 cited

CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

Zihao Wang, Wei Liu, Qian He +2

Training a text-to-image generator in the general domain (e.g., Dall.e, CogView) requires huge amounts of paired text-image data, which is too expensive to collect. In this paper,…