Introduces PexelsCustom-1M dataset, CustoMDiT parameter-efficient model, and OpenCustom benchmark for open-domain customized video generation.
Customcrafter: Customized video generation with preserving motion and concept composition abilities
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
CCDM uses attribute-decoupled LoRA with relevance-guided aggregation and controllable regional context synthesis to support incremental concept customization in diffusion models while mitigating catastrophic forgetting and concept neglect.
SynMotion combines disentangled semantic embeddings, parameter-efficient motion adapters, and alternate subject-motion training on a new SPV dataset to improve motion customization in text-to-video and image-to-video generation.
This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.
citing papers explorer
-
A Comprehensive Ecosystem for Open-Domain Customized Video Generation
Introduces PexelsCustom-1M dataset, CustoMDiT parameter-efficient model, and OpenCustom benchmark for open-domain customized video generation.
-
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
CCDM uses attribute-decoupled LoRA with relevance-guided aggregation and controllable regional context synthesis to support incremental concept customization in diffusion models while mitigating catastrophic forgetting and concept neglect.
-
SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation
SynMotion combines disentangled semantic embeddings, parameter-efficient motion adapters, and alternate subject-motion training on a new SPV dataset to improve motion customization in text-to-video and image-to-video generation.
-
Evolution of Video Generative Foundations
This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.